LAPORAN
PRAKTIKUM
laporan ini di ajukan untuk Memenihi Salah Satu Praktikum
Kecerdasan Buatan
Disusun oleh :
Asep muharam (0606012)
JURUSAN TEKNIK INFORMATIKA
SEKOLAH TINGGI TEKNOLOGI GARUT (STTG)
2009
Modul 1
Landasan Teori:
Fuzzy logic pertama kali dikenalkan kepada publik oleh Lotfi Zadeh, seorang profesor di University of California di Berkeley. Fuzzy logic digunakan untuk menyatakan hukum operasional dari suatu sistem dengan ungkapan bahasa, bukan dengan persamaan matematis Banyak sistem yang terlalu kompleks untuk. dimodelkan secara akurat, meskipun dengan persamaan matematis yang kompleks. Dalam kasus seperti itu, ungkapan bahasa yang digunakan dalam Fuzzy logic dapat membantu mendefinisikan karakteristik operasional sistem dengan lebih baik Ungkapan bahasa untuk karakteristik sistem biasanya dinyatakan dalam bentuk implikasi logika
Jawaban Pertanyaan
1. Cara-cara memasuki tool box fuzzy pada MATLAB 6.5 :
Membuka program MATLAB, kemudian kita mengklik menu start-toolboxes-fuzzy
Command line
Atau menuliskan fuzzy pada command line
System penalaran fuzzy yang baru pada command line, maka ketikan. >>Fuzzy, kemudian pada layer akan tampak FIS editor
2) 1. Fuzzy Inference System (FIS) Editor;
2. Membership Function Editor;
3. Rule Editor;
4. Rule Viewer;
5. Surface Viewer
3) Yang terdapat pada pop up menu And Method yaitu min, prod, custom
Menu pop up pada impilication sama dengan menu And method yaitu : min, prod, custom
Menu pop up pada agregation yaitu: max, sum, probor, Custom
Menu pop up yang terdapat pada defuzzyfication yaitu: centroid, bisector, mom, lom, som, custom
4) Untuk mengubah antar muka FIS Editor menjadi Membership function Editor yaitu dengan cara memilih menu Edit - Membership (ctrl+2) atau menekan double clik ikon variable input
Variabel input
5) Langkah untuk membuat grafik fungsi keanggotaan pada Membership function Editor yaitu ;
• Klik variable input atau output sampai ada warna merah pada bingkainya
• Isikan Rangenya sesuai dengan panjang jumlah data yang akan dimasukan
• Klik garis yang akan dibuat grafik hingga berwarna merah
• Tentukan fungsi keanggotannya
• Ubah paramsnya sesuai dengan tiap data yang akan dimasukan
7) a). Untuk membuat aturan pada rule editor yang telah dibuat maka proses yang harus dilakukan yaitu:
• Double klik kotak pada kotak aturan © FIS Editor atau pilih Edit rules pada menu view maka akan muncul rule editor
• Buat aturan yang telah dibuat pada rule Editor dengan cara klik untuk fungsi if, and, then pada kolom yang telah tersedia, sesuai dengan aturan yang dibuat
b) Untuk menambah aturan dengan cara mengklik tombol add rule dan untuk menghilangkan sebuah aturan pada rule editor yaitu dengan cara klik aturan yang akan dihapus kemudian klik tombol delete rule pada rule editor
8) Rule Viewer berfungsi untuk melihat alur penalaran fuzzy pada system, cara untuk memasuki rule viewer yaitu dengan memilih menu-view-rules atau menekan tombol ctrl+5
9) Surface Viewer berfungsi untuk melihat gambar pemetaan antara variabel-variabel input dan variable-variabel output.Untuk memasuki menu Surface Viewer yaitu dengan memilih menu view-view surface atau menekan tombol Ctrl+6
Analisis Modul 1 :
Kelebihan :
• Software matlab ini dapat membantu seorang user dalam memecahkan suatu masalah khususnya dalam logika fuzzy, dimana software ini menyediakan fasilitas jenis-jenis toolbox yang dapat mempermudah pekerjaan seorang user.
• Data yang dimasukan disajikan dalam bentuk kurva, sehingga dapat mempermudah dalam melihat akan hasil dari data yang dibuat.
Kelemahan :
• Di dalam mtlab membuat program berupa grafik, kurva
• Tidak bias membuat program bisnis
• Tidak bias membuat sebuah program animasi
MODUL 2
Landasan Teori
Fungsi keanggotaan adalah suatu fungsi yang mendefinisikan bagaimana memetakan titik-titik dalam ruang masukan ke dalam derajat keanggotaannya antara nilai 0 dan 1. Ruang masukan biasanya disebut juga sebagai semesta pembicaraan. Set fuzzy merupakan pengembangan dari set klasik jika X adalah semesta pembicaraan dan x menyatakan elemennya, maka set fuzzy A dalam X dinyatakan sebagai berikut [3] :
A = {X ,μ A (x) / x∈ X} (2.11)
μ A (x) disebut fungsi keanggotaan dari x dalam A. fungsi keanggotaan memetakan setiap elemen di dalam A. Fungsi keanggotaan memetaan setiap elemen dari X ke nilai keanggotaan antara 0 dan 1, secara matematis dinyatakan sebagai
:
μ A (x) :U→[0,1] (2.12)
Suatu set fuzzy A, dibentuk oleh gabungan dari xI yang masing-masing berderajat μ A (x) , dinyatakan sebagai :
Semua elemen dalam setiap x∈U yang memberikan nilai μ A (x) > 0 disebut support
dari himpunan fuzzy yang bersangkutan. jika μ A (x) = 0.5 maka x disebut titik crossover.
LISTING PROGRAM dan GRAFIK FUNGSI
1. Fungsi segitiga dengan parameter 5, 10, 15 dengan jarak 1
>> x=0:0.1:20;
>> y=trimf(x,[5 10 15]);
>> plot(x,y);grid;title;('FungsiSegitiga');xlabel('x');ylabel('Mu[x]');
>> trimf(5.25,[5 10 15])
ans =
0.0500
>> trimf(8.75,[5 10 15])
ans =
0.7500
>> trimf(12.5,[5 10 15])
ans =
0.5000
>> trimf(14.65,[5 10 15])
ans =
0.0700
Gambar Fungsi Segitiga
2. Fungsi trapesium dengan parameter 2, 6, 10, 14 dengan jarak 1
>> x=0:0.1:20;
>> y=trapmf(x,[2 6 10 14]);
>> plot(x,y);grid;title;('FungsiTrapesium');xlabel('x');ylabel('Mu[x]');
>> trapmf(5.25,[2 6 10 15)
>> trapmf(5.25,[2 6 10 15])
ans =
0.8125
>> trapmf(5.25,[2 6 10 14])
ans =
0.8125
>> trapmf(8.76,[2 6 10 14])
ans =
1
>> trapmf(12.5,[2 6 10 14])
ans =
0.3750
>> trapmf(8.75,[2 6 10 14])
ans =
1
Gambar Trapesium
3. Fungsi Keanggotaan Gausian bell dengan parameter 5, 8, 10 dengan jarak 0,5
>> x=0:0.5:25;
>> y=gbellmf(x,[5 8 10]);
>> plot(x,y);grid;title;('FungsigBell');xlabel('x');ylabel('Mu[x]');
>> gbellmf(5,[5 8 10])
ans =
0.5000
>> gbellmf(6.85,[5 8 10])
ans =
0.9994
>> gbellmf(8.58,[5 8 10])
ans =
1.0000
>> gbellmf(9.75,[5 8 10])
ans =
1
Gambar Gaussian Bell
4. Fungsi Keanggotaan Gausian dengan parameter 5, 8, 10 dengan jarak 0,5
>> x=0:0.1:20;
>> y=gaussmf(x,[6 10]);
>> plot(x,y);grid;title;('Fungsigauss');xlabel('x');ylabel('Mu[x]');
>> gaussmf(6.25,[6 10])
ans =
0.8226
>> gaussmf(6.85,[6 10])
ans =
0.8713
>> gaussmf(6.95,[6 10])
ans =
0.8788
>> gaussmf(9.15,[6 10])
ans =
0.9900
Gambar Gaussian
5. Fungsi Keanggotaan Gaussian2 dengan parameter sig1=4, c1=6, sig2=8, c2=10 dengan jarak 0,2
>> x=0:0.2:30;
>> y=gauss2mf(x,[4 6 8 10]);
>> plot(x,y);grid;title;('Fungsigauss2');xlabel('x');ylabel('Mu[x]');
>> gaussmf(4.55,[4 6 8 10])
ans =
0.9364
>> gaussmf(5.35,[4 6 8 10])
ans =
0.9869
>> gaussmf(7.75,[4 6 8 10])
ans =
0.9087
>> gaussmf(9.25,[4 6 8 10])
ans =
0.7189
Gambar Gaussian2
6. Fungsi Keanggotaan Phi dengan parameter 4, 5, 6, 9 dengan jarak 0,5
>> x=0:0.5:20;
>> y=pimf(x,[4 5 6 9]);
>> x=0:0.5:10;
>> y=pimf(x,[4 5 6 9]);
>> plot(x,y);grid;title;('Fungsipi');xlabel('x');ylabel('Mu[x]');
>> pimf(4.5,[4 5 6 9])
ans =
0.5000
>> pimf(5.25,[4 5 6 9])
ans =
1
>> pimf(7.45,[4 5 6 9])
ans =
0.5328
>> pimf(9.25,[4 5 6 9])
ans =
0
Gambar Phi
7. Fungsi Keanggotaan Sigmoid dengan parameter 4, 5, 6, 9 dengan jarak 0,5
>> x=0:0.5:40;
>> y=sigmf(x,[3 9]);
>> plot(x,y);grid;title;('Fungsisig');xlabel('x');ylabel('Mu[x]');
>> sigmf(4,[3 9])
ans =
3.0590e-007
>> sigmf(5.2,[3 9])
ans =
1.1195e-005
>> sigmf(6.25,[3 9])
ans =
2.6119e-004
>> sigmf(8.35,[3 9])
ans =
0.1246
Gambar Sigmoid
8. Fungsi Keanggotaan KurvaS dengan parameter 4, 9 dengan jarak 0,2
>> x=0:0.2:30;
>> y=kurv5mf(x,[4 9]);
>> y=smf(x,[4 9]);
>> plot(x,y);grid;title;('Fungsis');xlabel('x');ylabel('Mu[x]');
>> smf(5.2,[4 9])
ans =
0.1152
>> smf(7.34,[4 9])
ans =
0.7796
>> smf(8,[4 9])
ans =
0.9200
>> smf(8.75,[4 9])
ans =
0.9950
Gambar Kurva S
9. Fungsi Keanggotaan KurvaZ dengan parameter 2, 8 dengan jarak 0,2
>> x=0:0.2:10;
>> y=zmf(x,[2 8]);
>> plot(x,y);grid;title;('Fungsiz');xlabel('x');ylabel('Mu[x]');
>> zmf(3.5,[2 8])
ans =
0.8750
>> zmf(5.5,[2 8])
ans =
0.3472
>> zmf(6.25,[2 8])
ans =
0.1701
>> zmf(7.85,[2 8])
ans =
0.0013
>> zmf(8.75,[2 8])
ans =
0
Gambar Kurva Z
10. Fungsi Keanggotaan Sigmoid Ganda dengan parameter 6, 3, 6, 8 dengan jarak 0,2
>> x=0:0.2:30;
>> y=sigmf(x,[6 3 6 8]);
>> plot(x,y);grid;title;('Fungsisig');xlabel('x');ylabel('Mu[x]');
>> sigmf(6.5,[6 3 6 8])
ans =
1.0000
>> sigmf(6.35,[6 3 6 8])
ans =
1.0000
>> sigmf(7.25,[6 3 6 8])
ans =
1.0000
>> sigmf(7.65,[6 3 6 8])
ans =
1.0000
Gambar Sigmoid Ganda
11. Fungsi Keanggotaan Phi Sigmoid dengan parameter 6, 3, 6, 8 dengan jarak 0,2
>> x=0:0.2:10;
>> y=psigmf(x,[6 3 6 8]);
>> plot(x,y);grid;title;('Fungsipsig');xlabel('x');ylabel('Mu[x]');
>> psigmf(6.5,[6 3 6 8])
ans =
1.2339e-004
>> psigmf(6.35,[6 3 6 8])
ans =
5.0172e-005
>> psigmf(7.25,[6 3 6 8])
ans =
0.0110
>> psigmf(7.65,[6 3 6 8])
ans =
0.1091
Gambar Phi Sigmoid
Analisis :
Logika fuzzy menunjukkan bahwa bahwa pada fungsi keanggotaan tidak terpengaruh induksi medan manget. Hal ini juga ini perkuat dengan dengan hasil pengukuran, dimana nilai kuat medan magnet di tempat sampel berada masih tergolong aman.
MODUL 3
Landasan Teori
Salah satu sistem logika fuzzy yang paling banyak digunakan adalah sistem logika fuzy
Mamdani. Sistem fuzzy Mamdani terdiri dari [3] :
- basis aturan yang berisi sekumpulan aturan if-then
- basis data yang mendefinisikan fungsi keanggotaan himpunan fuzzy
- mesin inferensi yang melakukan operasi inferensi
- fuzzifikasi dan defuzzifikasi
Sistem fuzzy Mamdani menggunakan basis aturan seperti pada persamaan (2.25). Pada sistem fuzzy yang tertera pada gambar 2.5, terdapat banyak kebebasan dalam memilih jenis fuzzifikasi, defuzzifikasi dan mesin inferensi beserta operatornya sesuai dengan masalah yang dihadapi
.
Modul 3a
No Tingkat Pelayanan Tingkat Harga Kamar Tingkat Kepuasan Konsumen Kategori Tingkat Kepuasan
1 6 2,57 50 Cukup Puas
2 3,25 9,15 50 Puas
3 5,2 5,4 33,8 Cukup Puas
4 2,18 8,35 50 Cukup Puas
5 8,4 5,15 46,9 Cukup Puas
6 10 7,75 65 Puas
7 5,75 9,00 65 Puas
8 4,25 5,27 30 Cukup Puas
9 7,74 6,25 54,4 Puas
10 8,58 8,07 50 Puas
11 9,25 4,5 45 Cukup Puas
12 4,35 9,85 50 Puas
Modul3 B
Analisis :
Dari hasil dari pengolahan dengan menggunakan logika fuzzy ditunjukkan bahwa penduduk yang menjadi sampel tergolong pada fungsi keanggotaan yang tidak terpengaruh pada b. Maksudnya adalah pengaruh b disini tidak terlalu signifikan terdapat pola penyakit yang dapat membahayakan jiwa. Dari data riwayat kesehatan penduduk setempat (yang menjadi sampel) juga ditunjukkan tidak terlalu signifikannya perbedaan pola penyakit antara penduduk yang berada dibawah b dan di luar b.
Modul4
Landasan Teori
Pengenalan pola (pattern recognition) sesungguhnya telah lama ada dan telah mengalami perkembangan terus menerus dimulai dari pengenalan pola tradisional kemudian menjadi pengenalan pola modern. Pada mulanya pengenalan pola berbasis pada kemampuan alat indera manusia, dimana manusia mampu mengingat suatu informasi pola secara menyeluruh hanya berdasarkan sebagian informasi pola yang tersimpan di dalam ingatannya. Misalnya sebuah nada pendek yang dibunyikan dapat membuat kita mengingat sebuah lagu secara keseluruhan
Inti dari pengenalan pola adalah proses pengenalan suatu objek dengan menggunakan berbagai metode dimana dalam proses pengenalannya memiliki tingkat akurasi yang tinggi. Memiliki tingkat akurasi yang tinggi mengandung pengertian bahwa suatu objek yang secara manual (oleh manusia) tidak dapat dikenali tetapi bila menggunakan salah suatu metode pengenalan yang diaplikasikan pada komputer masih dapat dikenali
1)
2)
Analisis :
Fuzzifikasi memetakan titik crisp x di U ke dalam himpunan fuzzy A' pada U. Fuzzifikasi yang paling banyak digunakan adalah fuzzifikasi singleton dimana titik crisp x dipetakan menjadi himpunan fuzzy yang supportnya hanya pada satu titik x. Sedangkan fuzzifikasi non-singleton mempunyai support lebih dari satu titik [2]. Defuzzifikasi memetakan kembali besaran yang berupa himpunan fuzzy menjadi titik crisp. Defuzzifikasi dibutuhkan dalam penerapan sistem fuzzy karena yang digunakan dalam aplikasi adalah besaran crisp.
Modul 5
Landasan Teori
Perceptron adalah bentuk paling sederhana dari JST yang digunakan untuk pengklasifikasian jenis pola khusus yang biasa disebut linearly separable (pola-pola yang terletak pada sisi yang berlawanan pada suatu bidang).
Perceptron menggambarkan suatu usaha untuk membangun kecerdasan dan system pembelajaran-sendiri menggunakan komponen sederhana yang berasal dari model jaringan biologi yang diperkenalkan oleh McCuulloch dan Pitts (1943). Berikutnya Rosenblatt (1950) merancang perceptron dengan menguraikan pemodelan kemampuan sistim pengenalan pola untuk sistem penggambaran biologi.
Metode perceptron merupakan metode pembelajaran dengan pengawasan dalam sistim jaringan neural, sehingga jaringan yang dihasilkan harus mempunyai parameter yang dapat diatur dengan cara mengubah melalui aturan pembelajaran dengan pengawasan.
1. Perceptron mengenal fungsi logika AND 3 input dengan inisialisasi bobot bilangan Random.
Input Target
X1 X2 X3 T
0 0 0 0
0 0 1 0
0 1 0 0
0 1 1 0
1 0 0 0
1 0 1 0
1 1 0 0
1 1 1 1
2 Listing Program
>> nntwarn off
>> p=[0 0 0 0 1 1 1 1; 0 0 1 1 0 0 1 1; 0 1 0 1 0 1 0 1]
p =
0 0 0 0 1 1 1 1
0 0 1 1 0 0 1 1
0 1 0 1 0 1 0 1
>> t=[0 0 0 0 0 0 0 1]
t =
0 0 0 0 0 0 0 1
>> [w,b,]=initp(p,t)
>> [w,b,]=initp(p,t)
w =
0.6428 -0.1106 0.2309
b =
0.5839
>> tp=[1 275]
tp =
1 275
3)
>> [w,b]=trainp(w,b,p,t,tp)
TRAINP: 0/275 epochs, SSE = 7.
TRAINP: 1/275 epochs, SSE = 1.
TRAINP: 2/275 epochs, SSE = 1.
TRAINP: 3/275 epochs, SSE = 1.
TRAINP: 4/275 epochs, SSE = 1.
TRAINP: 5/275 epochs, SSE = 2.
TRAINP: 6/275 epochs, SSE = 1.
TRAINP: 7/275 epochs, SSE = 1.
TRAINP: 8/275 epochs, SSE = 3.
TRAINP: 9/275 epochs, SSE = 1.
TRAINP: 10/275 epochs, SSE = 1.
TRAINP: 11/275 epochs, SSE = 3.
TRAINP: 12/275 epochs, SSE = 1.
TRAINP: 13/275 epochs, SSE = 1.
TRAINP: 14/275 epochs, SSE = 0.
w =
1.6428 1.8894 2.2309
b =
-4.4161
3Inisialisasi Bobot dan Bias
A. Bobot awal dan Bias awal
w =
0.6428 -0.1106 0.2309
b =
0.5839
B. Bobot dan Bias Akhir
w =
1.6428 1.8894 2.2309
b =
-4.4161
4Gambar
5
Input T Perhitungan Jaringan
X1 X2 X3 T Net=X1W1+X2W2+b Fnet= 1 jika net >0
0 jika net <0
0 0 0 0 0 0
0 0 1 0 1 0
0 1 0 0 0 0
0 1 1 0 1 0
1 0 0 0 0 0
1 0 1 0 1 0
1 1 0 0 0 0
1 1 1 1 1 1
6analisis:
Proses pembelajaran merupakan suatu metoda untuk proses pengenalan suatu objek yang sifatnya kontinuitas yang selalu direspon secara berbeda dari setiap proses pembelajaran tersebut. Tujuan dari pembelajaran ini sebenarnya untuk memperkecil tingkat suatu error dalam pengenalan suatu objek.
1. Perceptron untuk Mengenal Fungsi logika OR 3 input dengan inisialisasi bilangan random
Input Target
X1 X2 X3 1
1 1 1 1
1 1 0 1
1 0 1 1
1 0 0 1
0 1 1 1
0 1 0 1
0 0 1 1
0 0 0 0
2. Listing Program
>> nntwarn off
>> p=[1 1 1 1 0 0 0 0; 1 1 0 0 1 1 0 0; 1 0 1 0 1 0 1 0]
p =
1 1 1 1 0 0 0 0
1 1 0 0 1 1 0 0
1 0 1 0 1 0 1 0
>> t=[1 1 1 1 1 1 1 0]
t =
1 1 1 1 1 1 1 0
>> [w,b,]=initp(p,t)
w =
0.9003 -0.5377 0.2137
b =
-0.0280
>> tp=[1 50]
tp =
1 50
>> [w,b]=trainp(w,b,p,t,tp)
TRAINP: 0/50 epochs, SSE = 2.
TRAINP: 1/50 epochs, SSE = 1.
TRAINP: 2/50 epochs, SSE = 1.
TRAINP: 3/50 epochs, SSE = 0.
w =
0.9003 1.4623 1.2137
b =
-0.0280
3Inisialisasi Bobot dan Bias
C. Bobot awal dan Bias awal
w =
0.9003 -0.5377 0.2137
b =
-0.0280
D. Bobot dan Bias Akhir
w =
0.9003 1.4623 1.2137
b =
-0.0280
4. Gambar
Input T Perhitungan Jaringan
X1 X2 X3 T Net=X1W1+X2W2+b Fnet= 1 jika net >0
0 jika net <0
0 0 0 0 0 0
0 0 1 0 1 0
0 1 0 0 0 0
0 1 1 0 1 0
1 0 0 0 0 0
1 0 1 0 1 0
1 1 0 0 0 0
1 1 1 1 1 1
Analisis :]
Sistem pembelajaran pada metoda Supervised learning adalah system pembelajaran yang mana, setiap pengetahuan yang akan diberikan kepada sistem, pada awalnya diberikan suatu acuan untuk memetakan suatu masukan menjadi suatu keluaran yang diinginkan. Proses pembelajaran ini akan terus dilakukan selama kondisi error atau kondisi yang diinginkan belum tercapai. Adapun setiap perolehan error akan dikalkulasikan untuk setiap pemrosesan hingga data atau nilai yang diinginkan telah tercapai.
1. 1Perceptron mengenal Pola 3 input dengan inisialisasi bobot bilangan Random.
Input Target
X1 X2 X3 T
1 0 1 1
1 0 0 0
0 1 0 0
0 0 1 0
0 0 1 0
0 1 0 1
1 1 1 1
1 1 0 1
2. Listing Program
>> nntwarn off
>> p=[1 1 0 0 0 0 1 1; 0 0 1 0 0 1 1 1; 1 0 0 1 1 0 1 0]
p =
1 1 0 0 0 0 1 1
0 0 1 0 0 1 1 1
1 0 0 1 1 0 1 0
>> t=[1 0 0 0 0 1 1 1]
t =
1 0 0 0 0 1 1 1
>> [w,b,]=initp(p,t)
w =
0.9003 -0.5377 0.2137
b =
-0.0280
>> tp=[1 150]
>> tp=[1 20]
tp =
1 20
>> [w,b]=trainp(w,b,p,t,tp)
TRAINP: 0/20 epochs, SSE = 1.
TRAINP: 1/20 epochs, SSE = 1.
TRAINP: 2/20 epochs, SSE = 1.
TRAINP: 3/20 epochs, SSE = 1.
TRAINP: 4/20 epochs, SSE = 1.
TRAINP: 5/20 epochs, SSE = 1.
TRAINP: 6/20 epochs, SSE = 1.
TRAINP: 7/20 epochs, SSE = 1.
TRAINP: 8/20 epochs, SSE = 1.
TRAINP: 9/20 epochs, SSE = 1.
TRAINP: 10/20 epochs, SSE = 1.
TRAINP: 11/20 epochs, SSE = 1.
TRAINP: 12/20 epochs, SSE = 1.
TRAINP: 13/20 epochs, SSE = 1.
TRAINP: 14/20 epochs, SSE = 1.
TRAINP: 15/20 epochs, SSE = 1.
TRAINP: 16/20 epochs, SSE = 1.
TRAINP: 17/20 epochs, SSE = 1.
TRAINP: 18/20 epochs, SSE = 1.
TRAINP: 19/20 epochs, SSE = 1.
TRAINP: 20/20 epochs, SSE = 1.
w =
3.9003 3.4623 1.2137
b =
-5.0280
Tidak Mengenal Target
3 Inisialisasi Bobot dan Bias
E. Bobot awal dan Bias awal
w =
0.9003 -0.5377 0.2137
b =
-0.0280
F. Bobot dan Bias Akhir
w =
3.9003 3.4623 1.2137
b =
-5.0280
4. Gambar
5)
Input T Perhitungan Jaringan
X1 X2 X3 T Net=X1W1+X2W2+b Fnet= 1 jika net >0
0 jika net <0
0 0 0 0 0 0
0 0 1 0 1 0
0 1 0 0 0 0
0 1 1 0 1 0
1 0 0 0 0 0
1 0 1 0 1 0
1 1 0 0 0 0
1 1 1 1 1 1
Analisis :
Sistem pembelajaran pada neural network, yang mana sistem ini memberikan sepenuhnya pada hasil komputasi dari setiap pemrosesan, sehingga pada sistem ini tidak membutuhkan adanya acuan awal agar perolehan nilai dapat dicapai. Meskipun secara mendasar, proses ini tetap mengkalkulasikan setiap langkah pada setiap kesalahannya dengan mengkalkulasikan setiap nilai weight yang didapat.
4)
>> nntwarn off
>> p=[[9;6] [7;2] [4;5] [3;10] [1;8] [1;7] [8;4] [5;6]]
p =
9 7 4 3 1 1 8 5
6 2 5 10 8 7 4 6
>> t=[0 1 1 0 1 1 0 0]
t =
0 1 1 0 1 1 0 0
>> [w,b]=initp(p,t)
w =
0.9003 -0.5377
b =
0.2137
>> tp=[1 500]
tp =
1 500
>> [w,b]=trainp(w,b,p,t,tp)
TRAINP: 0/500 epochs, SSE = 4.
TRAINP: 1/500 epochs, SSE = 4.
TRAINP: 2/500 epochs, SSE = 5.
TRAINP: 3/500 epochs, SSE = 4.
TRAINP: 4/500 epochs, SSE = 3.
TRAINP: 5/500 epochs, SSE = 2.
TRAINP: 6/500 epochs, SSE = 4.
TRAINP: 7/500 epochs, SSE = 4.
TRAINP: 8/500 epochs, SSE = 2.
TRAINP: 9/500 epochs, SSE = 4.
TRAINP: 10/500 epochs, SSE = 4.
TRAINP: 11/500 epochs, SSE = 3.
TRAINP: 12/500 epochs, SSE = 3.
TRAINP: 13/500 epochs, SSE = 4.
TRAINP: 14/500 epochs, SSE = 4.
TRAINP: 15/500 epochs, SSE = 4.
TRAINP: 16/500 epochs, SSE = 4.
TRAINP: 17/500 epochs, SSE = 4.
TRAINP: 18/500 epochs, SSE = 3.
TRAINP: 19/500 epochs, SSE = 2.
TRAINP: 20/500 epochs, SSE = 4.
TRAINP: 21/500 epochs, SSE = 4.
TRAINP: 22/500 epochs, SSE = 3.
TRAINP: 23/500 epochs, SSE = 2.
TRAINP: 24/500 epochs, SSE = 4.
TRAINP: 25/500 epochs, SSE = 4.
TRAINP: 26/500 epochs, SSE = 4.
TRAINP: 27/500 epochs, SSE = 4.
TRAINP: 28/500 epochs, SSE = 4.
TRAINP: 29/500 epochs, SSE = 3.
TRAINP: 30/500 epochs, SSE = 4.
TRAINP: 31/500 epochs, SSE = 4.
TRAINP: 32/500 epochs, SSE = 4.
TRAINP: 33/500 epochs, SSE = 3.
TRAINP: 34/500 epochs, SSE = 4.
TRAINP: 35/500 epochs, SSE = 4.
TRAINP: 36/500 epochs, SSE = 4.
TRAINP: 37/500 epochs, SSE = 3.
TRAINP: 38/500 epochs, SSE = 2.
TRAINP: 39/500 epochs, SSE = 4.
TRAINP: 40/500 epochs, SSE = 4.
TRAINP: 41/500 epochs, SSE = 3.
TRAINP: 42/500 epochs, SSE = 3.
TRAINP: 43/500 epochs, SSE = 4.
TRAINP: 44/500 epochs, SSE = 4.
TRAINP: 45/500 epochs, SSE = 4.
TRAINP: 46/500 epochs, SSE = 3.
TRAINP: 47/500 epochs, SSE = 4.
TRAINP: 48/500 epochs, SSE = 4.
TRAINP: 49/500 epochs, SSE = 4.
TRAINP: 50/500 epochs, SSE = 4.
TRAINP: 51/500 epochs, SSE = 4.
TRAINP: 52/500 epochs, SSE = 3.
TRAINP: 53/500 epochs, SSE = 2.
TRAINP: 54/500 epochs, SSE = 4.
TRAINP: 55/500 epochs, SSE = 4.
TRAINP: 56/500 epochs, SSE = 4.
TRAINP: 57/500 epochs, SSE = 4.
TRAINP: 58/500 epochs, SSE = 4.
TRAINP: 59/500 epochs, SSE = 3.
TRAINP: 60/500 epochs, SSE = 4.
TRAINP: 61/500 epochs, SSE = 4.
TRAINP: 62/500 epochs, SSE = 4.
TRAINP: 63/500 epochs, SSE = 3.
TRAINP: 64/500 epochs, SSE = 2.
TRAINP: 65/500 epochs, SSE = 4.
TRAINP: 66/500 epochs, SSE = 4.
TRAINP: 67/500 epochs, SSE = 2.
TRAINP: 68/500 epochs, SSE = 4.
TRAINP: 69/500 epochs, SSE = 4.
TRAINP: 70/500 epochs, SSE = 3.
TRAINP: 71/500 epochs, SSE = 2.
TRAINP: 72/500 epochs, SSE = 4.
TRAINP: 73/500 epochs, SSE = 4.
TRAINP: 74/500 epochs, SSE = 4.
TRAINP: 75/500 epochs, SSE = 4.
TRAINP: 76/500 epochs, SSE = 4.
TRAINP: 77/500 epochs, SSE = 2.
TRAINP: 78/500 epochs, SSE = 2.
TRAINP: 79/500 epochs, SSE = 4.
TRAINP: 80/500 epochs, SSE = 4.
TRAINP: 81/500 epochs, SSE = 2.
TRAINP: 82/500 epochs, SSE = 3.
TRAINP: 83/500 epochs, SSE = 4.
TRAINP: 84/500 epochs, SSE = 4.
TRAINP: 85/500 epochs, SSE = 3.
TRAINP: 86/500 epochs, SSE = 4.
TRAINP: 87/500 epochs, SSE = 4.
TRAINP: 88/500 epochs, SSE = 4.
TRAINP: 89/500 epochs, SSE = 3.
TRAINP: 90/500 epochs, SSE = 2.
TRAINP: 91/500 epochs, SSE = 4.
TRAINP: 92/500 epochs, SSE = 4.
TRAINP: 93/500 epochs, SSE = 2.
TRAINP: 94/500 epochs, SSE = 4.
TRAINP: 95/500 epochs, SSE = 4.
TRAINP: 96/500 epochs, SSE = 2.
TRAINP: 97/500 epochs, SSE = 3.
TRAINP: 98/500 epochs, SSE = 4.
TRAINP: 99/500 epochs, SSE = 4.
TRAINP: 100/500 epochs, SSE = 3.
TRAINP: 101/500 epochs, SSE = 4.
TRAINP: 102/500 epochs, SSE = 4.
TRAINP: 103/500 epochs, SSE = 4.
TRAINP: 104/500 epochs, SSE = 4.
TRAINP: 105/500 epochs, SSE = 4.
TRAINP: 106/500 epochs, SSE = 3.
TRAINP: 107/500 epochs, SSE = 2.
TRAINP: 108/500 epochs, SSE = 2.
TRAINP: 109/500 epochs, SSE = 4.
TRAINP: 110/500 epochs, SSE = 4.
TRAINP: 111/500 epochs, SSE = 1.
TRAINP: 112/500 epochs, SSE = 4.
TRAINP: 113/500 epochs, SSE = 4.
TRAINP: 114/500 epochs, SSE = 2.
TRAINP: 115/500 epochs, SSE = 4.
TRAINP: 116/500 epochs, SSE = 4.
TRAINP: 117/500 epochs, SSE = 2.
TRAINP: 118/500 epochs, SSE = 4.
TRAINP: 119/500 epochs, SSE = 4.
TRAINP: 120/500 epochs, SSE = 2.
TRAINP: 121/500 epochs, SSE = 3.
TRAINP: 122/500 epochs, SSE = 4.
TRAINP: 123/500 epochs, SSE = 4.
TRAINP: 124/500 epochs, SSE = 3.
TRAINP: 125/500 epochs, SSE = 4.
TRAINP: 126/500 epochs, SSE = 4.
TRAINP: 127/500 epochs, SSE = 4.
TRAINP: 128/500 epochs, SSE = 3.
TRAINP: 129/500 epochs, SSE = 2.
TRAINP: 130/500 epochs, SSE = 4.
TRAINP: 131/500 epochs, SSE = 4.
TRAINP: 132/500 epochs, SSE = 2.
TRAINP: 133/500 epochs, SSE = 4.
TRAINP: 134/500 epochs, SSE = 4.
TRAINP: 135/500 epochs, SSE = 2.
TRAINP: 136/500 epochs, SSE = 2.
TRAINP: 137/500 epochs, SSE = 4.
TRAINP: 138/500 epochs, SSE = 4.
TRAINP: 139/500 epochs, SSE = 2.
TRAINP: 140/500 epochs, SSE = 4.
TRAINP: 141/500 epochs, SSE = 4.
TRAINP: 142/500 epochs, SSE = 2.
TRAINP: 143/500 epochs, SSE = 3.
TRAINP: 144/500 epochs, SSE = 4.
TRAINP: 145/500 epochs, SSE = 4.
TRAINP: 146/500 epochs, SSE = 2.
TRAINP: 147/500 epochs, SSE = 4.
TRAINP: 148/500 epochs, SSE = 4.
TRAINP: 149/500 epochs, SSE = 4.
TRAINP: 150/500 epochs, SSE = 3.
TRAINP: 151/500 epochs, SSE = 4.
TRAINP: 152/500 epochs, SSE = 4.
TRAINP: 153/500 epochs, SSE = 4.
TRAINP: 154/500 epochs, SSE = 3.
TRAINP: 155/500 epochs, SSE = 2.
TRAINP: 156/500 epochs, SSE = 4.
TRAINP: 157/500 epochs, SSE = 4.
TRAINP: 158/500 epochs, SSE = 2.
TRAINP: 159/500 epochs, SSE = 4.
TRAINP: 160/500 epochs, SSE = 4.
TRAINP: 161/500 epochs, SSE = 2.
TRAINP: 162/500 epochs, SSE = 2.
TRAINP: 163/500 epochs, SSE = 4.
TRAINP: 164/500 epochs, SSE = 4.
TRAINP: 165/500 epochs, SSE = 2.
TRAINP: 166/500 epochs, SSE = 4.
TRAINP: 167/500 epochs, SSE = 4.
TRAINP: 168/500 epochs, SSE = 1.
TRAINP: 169/500 epochs, SSE = 2.
TRAINP: 170/500 epochs, SSE = 4.
TRAINP: 171/500 epochs, SSE = 4.
TRAINP: 172/500 epochs, SSE = 4.
TRAINP: 173/500 epochs, SSE = 3.
TRAINP: 174/500 epochs, SSE = 4.
TRAINP: 175/500 epochs, SSE = 4.
TRAINP: 176/500 epochs, SSE = 4.
TRAINP: 177/500 epochs, SSE = 3.
TRAINP: 178/500 epochs, SSE = 2.
TRAINP: 179/500 epochs, SSE = 3.
TRAINP: 180/500 epochs, SSE = 4.
TRAINP: 181/500 epochs, SSE = 2.
TRAINP: 182/500 epochs, SSE = 4.
TRAINP: 183/500 epochs, SSE = 4.
TRAINP: 184/500 epochs, SSE = 4.
TRAINP: 185/500 epochs, SSE = 3.
TRAINP: 186/500 epochs, SSE = 3.
TRAINP: 187/500 epochs, SSE = 4.
TRAINP: 188/500 epochs, SSE = 4.
TRAINP: 189/500 epochs, SSE = 2.
TRAINP: 190/500 epochs, SSE = 2.
TRAINP: 191/500 epochs, SSE = 4.
TRAINP: 192/500 epochs, SSE = 4.
TRAINP: 193/500 epochs, SSE = 1.
TRAINP: 194/500 epochs, SSE = 3.
TRAINP: 195/500 epochs, SSE = 4.
TRAINP: 196/500 epochs, SSE = 2.
TRAINP: 197/500 epochs, SSE = 4.
TRAINP: 198/500 epochs, SSE = 4.
TRAINP: 199/500 epochs, SSE = 4.
TRAINP: 200/500 epochs, SSE = 3.
TRAINP: 201/500 epochs, SSE = 4.
TRAINP: 202/500 epochs, SSE = 4.
TRAINP: 203/500 epochs, SSE = 4.
TRAINP: 204/500 epochs, SSE = 3.
TRAINP: 205/500 epochs, SSE = 2.
TRAINP: 206/500 epochs, SSE = 3.
TRAINP: 207/500 epochs, SSE = 4.
TRAINP: 208/500 epochs, SSE = 3.
TRAINP: 209/500 epochs, SSE = 4.
TRAINP: 210/500 epochs, SSE = 2.
TRAINP: 211/500 epochs, SSE = 4.
TRAINP: 212/500 epochs, SSE = 4.
TRAINP: 213/500 epochs, SSE = 4.
TRAINP: 214/500 epochs, SSE = 3.
TRAINP: 215/500 epochs, SSE = 2.
TRAINP: 216/500 epochs, SSE = 2.
TRAINP: 217/500 epochs, SSE = 4.
TRAINP: 218/500 epochs, SSE = 4.
TRAINP: 219/500 epochs, SSE = 4.
TRAINP: 220/500 epochs, SSE = 3.
TRAINP: 221/500 epochs, SSE = 4.
TRAINP: 222/500 epochs, SSE = 4.
TRAINP: 223/500 epochs, SSE = 4.
TRAINP: 224/500 epochs, SSE = 3.
TRAINP: 225/500 epochs, SSE = 2.
TRAINP: 226/500 epochs, SSE = 3.
TRAINP: 227/500 epochs, SSE = 4.
TRAINP: 228/500 epochs, SSE = 3.
TRAINP: 229/500 epochs, SSE = 4.
TRAINP: 230/500 epochs, SSE = 2.
TRAINP: 231/500 epochs, SSE = 4.
TRAINP: 232/500 epochs, SSE = 4.
TRAINP: 233/500 epochs, SSE = 4.
TRAINP: 234/500 epochs, SSE = 3.
TRAINP: 235/500 epochs, SSE = 2.
TRAINP: 236/500 epochs, SSE = 2.
TRAINP: 237/500 epochs, SSE = 4.
TRAINP: 238/500 epochs, SSE = 4.
TRAINP: 239/500 epochs, SSE = 4.
TRAINP: 240/500 epochs, SSE = 3.
TRAINP: 241/500 epochs, SSE = 4.
TRAINP: 242/500 epochs, SSE = 4.
TRAINP: 243/500 epochs, SSE = 4.
TRAINP: 244/500 epochs, SSE = 3.
TRAINP: 245/500 epochs, SSE = 2.
TRAINP: 246/500 epochs, SSE = 3.
TRAINP: 247/500 epochs, SSE = 4.
TRAINP: 248/500 epochs, SSE = 3.
TRAINP: 249/500 epochs, SSE = 4.
TRAINP: 250/500 epochs, SSE = 2.
TRAINP: 251/500 epochs, SSE = 4.
TRAINP: 252/500 epochs, SSE = 4.
TRAINP: 253/500 epochs, SSE = 4.
TRAINP: 254/500 epochs, SSE = 3.
TRAINP: 255/500 epochs, SSE = 2.
TRAINP: 256/500 epochs, SSE = 2.
TRAINP: 257/500 epochs, SSE = 4.
TRAINP: 258/500 epochs, SSE = 4.
TRAINP: 259/500 epochs, SSE = 4.
TRAINP: 260/500 epochs, SSE = 3.
TRAINP: 261/500 epochs, SSE = 4.
TRAINP: 262/500 epochs, SSE = 4.
TRAINP: 263/500 epochs, SSE = 4.
TRAINP: 264/500 epochs, SSE = 2.
TRAINP: 265/500 epochs, SSE = 4.
TRAINP: 266/500 epochs, SSE = 4.
TRAINP: 267/500 epochs, SSE = 4.
TRAINP: 268/500 epochs, SSE = 3.
TRAINP: 269/500 epochs, SSE = 2.
TRAINP: 270/500 epochs, SSE = 2.
TRAINP: 271/500 epochs, SSE = 4.
TRAINP: 272/500 epochs, SSE = 4.
TRAINP: 273/500 epochs, SSE = 4.
TRAINP: 274/500 epochs, SSE = 2.
TRAINP: 275/500 epochs, SSE = 1.
TRAINP: 276/500 epochs, SSE = 1.
TRAINP: 277/500 epochs, SSE = 2.
TRAINP: 278/500 epochs, SSE = 4.
TRAINP: 279/500 epochs, SSE = 4.
TRAINP: 280/500 epochs, SSE = 1.
TRAINP: 281/500 epochs, SSE = 2.
TRAINP: 282/500 epochs, SSE = 4.
TRAINP: 283/500 epochs, SSE = 4.
TRAINP: 284/500 epochs, SSE = 4.
TRAINP: 285/500 epochs, SSE = 3.
TRAINP: 286/500 epochs, SSE = 3.
TRAINP: 287/500 epochs, SSE = 4.
TRAINP: 288/500 epochs, SSE = 4.
TRAINP: 289/500 epochs, SSE = 2.
TRAINP: 290/500 epochs, SSE = 1.
TRAINP: 291/500 epochs, SSE = 1.
TRAINP: 292/500 epochs, SSE = 3.
TRAINP: 293/500 epochs, SSE = 4.
TRAINP: 294/500 epochs, SSE = 4.
TRAINP: 295/500 epochs, SSE = 2.
TRAINP: 296/500 epochs, SSE = 4.
TRAINP: 297/500 epochs, SSE = 4.
TRAINP: 298/500 epochs, SSE = 4.
TRAINP: 299/500 epochs, SSE = 2.
TRAINP: 300/500 epochs, SSE = 1.
TRAINP: 301/500 epochs, SSE = 1.
TRAINP: 302/500 epochs, SSE = 2.
TRAINP: 303/500 epochs, SSE = 4.
TRAINP: 304/500 epochs, SSE = 4.
TRAINP: 305/500 epochs, SSE = 1.
TRAINP: 306/500 epochs, SSE = 1.
TRAINP: 307/500 epochs, SSE = 3.
TRAINP: 308/500 epochs, SSE = 4.
TRAINP: 309/500 epochs, SSE = 4.
TRAINP: 310/500 epochs, SSE = 2.
TRAINP: 311/500 epochs, SSE = 4.
TRAINP: 312/500 epochs, SSE = 4.
TRAINP: 313/500 epochs, SSE = 4.
TRAINP: 314/500 epochs, SSE = 3.
TRAINP: 315/500 epochs, SSE = 3.
TRAINP: 316/500 epochs, SSE = 3.
TRAINP: 317/500 epochs, SSE = 4.
TRAINP: 318/500 epochs, SSE = 3.
TRAINP: 319/500 epochs, SSE = 4.
TRAINP: 320/500 epochs, SSE = 2.
TRAINP: 321/500 epochs, SSE = 2.
TRAINP: 322/500 epochs, SSE = 2.
TRAINP: 323/500 epochs, SSE = 4.
TRAINP: 324/500 epochs, SSE = 4.
TRAINP: 325/500 epochs, SSE = 4.
TRAINP: 326/500 epochs, SSE = 3.
TRAINP: 327/500 epochs, SSE = 3.
TRAINP: 328/500 epochs, SSE = 3.
TRAINP: 329/500 epochs, SSE = 4.
TRAINP: 330/500 epochs, SSE = 3.
TRAINP: 331/500 epochs, SSE = 4.
TRAINP: 332/500 epochs, SSE = 3.
TRAINP: 333/500 epochs, SSE = 4.
TRAINP: 334/500 epochs, SSE = 2.
TRAINP: 335/500 epochs, SSE = 2.
TRAINP: 336/500 epochs, SSE = 2.
TRAINP: 337/500 epochs, SSE = 4.
TRAINP: 338/500 epochs, SSE = 4.
TRAINP: 339/500 epochs, SSE = 4.
TRAINP: 340/500 epochs, SSE = 2.
TRAINP: 341/500 epochs, SSE = 1.
TRAINP: 342/500 epochs, SSE = 1.
TRAINP: 343/500 epochs, SSE = 1.
TRAINP: 344/500 epochs, SSE = 1.
TRAINP: 345/500 epochs, SSE = 2.
TRAINP: 346/500 epochs, SSE = 3.
TRAINP: 347/500 epochs, SSE = 4.
TRAINP: 348/500 epochs, SSE = 3.
TRAINP: 349/500 epochs, SSE = 4.
TRAINP: 350/500 epochs, SSE = 2.
TRAINP: 351/500 epochs, SSE = 3.
TRAINP: 352/500 epochs, SSE = 3.
TRAINP: 353/500 epochs, SSE = 4.
TRAINP: 354/500 epochs, SSE = 3.
TRAINP: 355/500 epochs, SSE = 4.
TRAINP: 356/500 epochs, SSE = 2.
TRAINP: 357/500 epochs, SSE = 2.
TRAINP: 358/500 epochs, SSE = 1.
TRAINP: 359/500 epochs, SSE = 1.
TRAINP: 360/500 epochs, SSE = 1.
TRAINP: 361/500 epochs, SSE = 3.
TRAINP: 362/500 epochs, SSE = 4.
TRAINP: 363/500 epochs, SSE = 4.
TRAINP: 364/500 epochs, SSE = 2.
TRAINP: 365/500 epochs, SSE = 4.
TRAINP: 366/500 epochs, SSE = 3.
TRAINP: 367/500 epochs, SSE = 4.
TRAINP: 368/500 epochs, SSE = 3.
TRAINP: 369/500 epochs, SSE = 4.
TRAINP: 370/500 epochs, SSE = 2.
TRAINP: 371/500 epochs, SSE = 2.
TRAINP: 372/500 epochs, SSE = 2.
TRAINP: 373/500 epochs, SSE = 4.
TRAINP: 374/500 epochs, SSE = 4.
TRAINP: 375/500 epochs, SSE = 4.
TRAINP: 376/500 epochs, SSE = 3.
TRAINP: 377/500 epochs, SSE = 3.
TRAINP: 378/500 epochs, SSE = 3.
TRAINP: 379/500 epochs, SSE = 4.
TRAINP: 380/500 epochs, SSE = 3.
TRAINP: 381/500 epochs, SSE = 4.
TRAINP: 382/500 epochs, SSE = 3.
TRAINP: 383/500 epochs, SSE = 3.
TRAINP: 384/500 epochs, SSE = 2.
TRAINP: 385/500 epochs, SSE = 4.
TRAINP: 386/500 epochs, SSE = 3.
TRAINP: 387/500 epochs, SSE = 3.
TRAINP: 388/500 epochs, SSE = 2.
TRAINP: 389/500 epochs, SSE = 3.
TRAINP: 390/500 epochs, SSE = 2.
TRAINP: 391/500 epochs, SSE = 3.
TRAINP: 392/500 epochs, SSE = 3.
TRAINP: 393/500 epochs, SSE = 4.
TRAINP: 394/500 epochs, SSE = 3.
TRAINP: 395/500 epochs, SSE = 4.
TRAINP: 396/500 epochs, SSE = 2.
TRAINP: 397/500 epochs, SSE = 2.
TRAINP: 398/500 epochs, SSE = 1.
TRAINP: 399/500 epochs, SSE = 1.
TRAINP: 400/500 epochs, SSE = 1.
TRAINP: 401/500 epochs, SSE = 3.
TRAINP: 402/500 epochs, SSE = 4.
TRAINP: 403/500 epochs, SSE = 4.
TRAINP: 404/500 epochs, SSE = 1.
TRAINP: 405/500 epochs, SSE = 1.
TRAINP: 406/500 epochs, SSE = 0.
w =
-24.0997 -20.5377
b =
239.2137
3Inisialisasi Bobot dan Bias
G. Bobot awal dan Bias awal
w =
0.9003 -0.5377
b =
0.2137
H. Bobot dan Bias Akhir
w =
-24.0997 -20.5377
b =
239.2137
Input T Perhitungan Jaringan
X1 X2 X3 T Net=X1W1+X2W2+b Fnet= 1 jika net >0
0 jika net <0
0 0 0 0 O 0
0 0 1 0 0 0
0 1 0 0 0 0
0 1 1 0 0 0
1 0 0 0 0 0
1 0 1 0 0 0
1 1 0 0 0 0
1 1 1 1 1 1
Analisis :
Proses ini merupakan bagian dari sistem kerja secara keseluruhan, karena proses masukan digunakan untuk menunjang pada proses pembelajaran serta proses pengujian. Pada proses ini, masukan diklasifikasikan berdasarkan keinginan dari pembuat, dimana bentuk masukan dapat berupa nilai logic atau bilangan biner ( 1 atau 0 ),
Modul 6
Jaringan syaraf tiruan telah dikembangkan sejak tahun 1940. Pada tahun 1943 McCulloch dan W.H.Pitts memperkenalkan pemodelan matematis neuron. Tahun 1949, Hebb mencoba mengkaji proses belajar yang dilakukan oleh neuron. Teori ini dikenal sebagai Hebbian Law. Tahun 1958, Rosenblatt memperkenalkan konsep perseptron suatu jaringan yang terdiri dari beberapa lapisan yang saling berhubunganmelalui umpan maju (feed foward). Konsep ini dimaksudkan untuk memberikan ilustrasi tentang dasar-dasar intelejensia secara umum. Hasil kerja Rosenblatt yang sangat penting adalah perceptron convergence theorem (tahun 1962) yang membuktikan bahwa bila setiap perseptron dapat memilah-milah dua buah pola yang berbeda maka siklus pelatihannya dapat dilakukan dalam jumlah yang ter batas.
1)
>> nntwarn off
>> p=[0 0 1 1;0 1 0 1]
p =
0 0 1 1
0 1 0 1
>> t=[0 1 1 0]
t =
0 1 1 0
>> tp=[1 2000 0.35 0.01]
tp =
1.0e+003 *
0.0010 2.0000 0.0004 0.0000
tp =
1.0e+003 *
0.0010 2.0000 0.0004 0.0000
w1 =
2.2404 1.6795
-1.7588 -2.1786
-0.4883 2.7571
2.6840 0.7976
b1 =
-2.9854
1.1487
-0.8343
-1.3771
w2 =
-0.1879 0.1090 -0.0705 -0.6617
b2 =
-0.6619
TRAINBP: 0/2000 epochs, SSE = 5.85043.
TRAINBP: 1/2000 epochs, SSE = 5.68409.
TRAINBP: 2/2000 epochs, SSE = 5.51676.
TRAINBP: 3/2000 epochs, SSE = 5.34976.
TRAINBP: 4/2000 epochs, SSE = 5.18419.
TRAINBP: 5/2000 epochs, SSE = 5.02085.
TRAINBP: 6/2000 epochs, SSE = 4.86023.
TRAINBP: 7/2000 epochs, SSE = 4.70245.
TRAINBP: 8/2000 epochs, SSE = 4.54735.
TRAINBP: 9/2000 epochs, SSE = 4.39453.
TRAINBP: 10/2000 epochs, SSE = 4.24343.
TRAINBP: 11/2000 epochs, SSE = 4.09338.
TRAINBP: 12/2000 epochs, SSE = 3.94369.
TRAINBP: 13/2000 epochs, SSE = 3.7937.
TRAINBP: 14/2000 epochs, SSE = 3.64283.
TRAINBP: 15/2000 epochs, SSE = 3.49063.
TRAINBP: 16/2000 epochs, SSE = 3.33683.
TRAINBP: 17/2000 epochs, SSE = 3.18135.
TRAINBP: 18/2000 epochs, SSE = 3.02431.
TRAINBP: 19/2000 epochs, SSE = 2.86611.
TRAINBP: 20/2000 epochs, SSE = 2.70734.
TRAINBP: 21/2000 epochs, SSE = 2.54884.
TRAINBP: 22/2000 epochs, SSE = 2.39159.
TRAINBP: 23/2000 epochs, SSE = 2.23674.
TRAINBP: 24/2000 epochs, SSE = 2.08546.
TRAINBP: 25/2000 epochs, SSE = 1.93894.
TRAINBP: 26/2000 epochs, SSE = 1.79827.
TRAINBP: 27/2000 epochs, SSE = 1.66439.
TRAINBP: 28/2000 epochs, SSE = 1.53806.
TRAINBP: 29/2000 epochs, SSE = 1.41983.
TRAINBP: 30/2000 epochs, SSE = 1.31002.
TRAINBP: 31/2000 epochs, SSE = 1.20874.
TRAINBP: 32/2000 epochs, SSE = 1.11591.
TRAINBP: 33/2000 epochs, SSE = 1.03129.
TRAINBP: 34/2000 epochs, SSE = 0.954509.
TRAINBP: 35/2000 epochs, SSE = 0.885129.
TRAINBP: 36/2000 epochs, SSE = 0.822634.
TRAINBP: 37/2000 epochs, SSE = 0.766485.
TRAINBP: 38/2000 epochs, SSE = 0.716133.
TRAINBP: 39/2000 epochs, SSE = 0.671037.
TRAINBP: 40/2000 epochs, SSE = 0.63068.
TRAINBP: 41/2000 epochs, SSE = 0.594572.
TRAINBP: 42/2000 epochs, SSE = 0.562261.
TRAINBP: 43/2000 epochs, SSE = 0.53333.
TRAINBP: 44/2000 epochs, SSE = 0.507403.
TRAINBP: 45/2000 epochs, SSE = 0.484139.
TRAINBP: 46/2000 epochs, SSE = 0.463234.
TRAINBP: 47/2000 epochs, SSE = 0.444417.
TRAINBP: 48/2000 epochs, SSE = 0.427447.
TRAINBP: 49/2000 epochs, SSE = 0.412111.
TRAINBP: 50/2000 epochs, SSE = 0.398222.
TRAINBP: 51/2000 epochs, SSE = 0.385613.
TRAINBP: 52/2000 epochs, SSE = 0.37414.
TRAINBP: 53/2000 epochs, SSE = 0.363674.
TRAINBP: 54/2000 epochs, SSE = 0.354102.
TRAINBP: 55/2000 epochs, SSE = 0.345325.
w1 =
2.1330 1.6388
-1.8304 -2.1898
-0.4893 2.7566
2.6162 0.7092
b1 =
-3.1092
1.0802
-0.8348
-1.5167
w2 =
-0.4818 -0.5585 -0.0283 -0.1979
b2 =
-0.0072
2)
>> clear
>> nntwarn off
>> p= [0 1 2 1 10 12 -5 -8 -10 -15; 0 1 -1 6 3 -1 -2 2 -5 2]
p =
Columns 1 through 9
0 1 2 1 10 12 -5 -8 -10
0 1 -1 6 3 -1 -2 2 -5
Column 10
-15
2
>> t=[0 0 1 1 2 2 -1 -1 -2 -2];
>> net=newff(minmax(p),[5 1],{'tansig' 'purelin'});
> BobotAwal_Input=net.IW{1,1}
BobotAwal_Input =
0.2004 0.2864
-0.2289 -0.0910
0.0502 -0.5557
-0.0101 0.5686
0.2296 -0.0796
>> BobotAwal_Bias_Input=net.b{1,1}
BobotAwal_Bias_Input =
-2.9731
1.2674
0.3532
-1.8647
3.5147
>> BobotAwal_Lapisan=net.LW{2,1}
BobotAwal_Lapisan =
0.2309 0.5839 0.8436 0.4764 -0.6475
>> BobotAwal_Bias_Lapisan=net.b{2,1}
BobotAwal_Bias_Lapisan =
-0.1886
>> net.adaptFcn='adaptwb';
>> net.inputWeights{1, 1}. learnFcn='learngdm';
>> net.layerWeights{2, 1}. learnFcn='learngdm';
>> net.biases{1, 1}. learnFcn='learngdm';
>> net.biases{2, 1}. learnFcn='learngdm';
>> net.inputWeights{1, 1}. learnParam.ir=0.02;
>> net.layerWeights{2, 1}. learnParam.ir=0.02;
>> net.biases{1, 1}. learnParam.ir=0.02;
>> net.inputWeights{1, 1}. learnParam.mc=0.3;
>> net.layerWeights{2, 1}. learnParam.mc=0.3;
>> net.biases{1, 1}. learnParam.mc=0.3;
>> net.adaptParam.passes=100;
>> p=num2cell(p,1);
>> t=num2cell(t,1);
>> [net, Y, e]=adapt(net,p,t)
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 2
biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]
targetConnect: [0 1]
numOutputs: 1 (read-only)
numTargets: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {2x1 cell} of layers
outputs: {1x2 cell} containing 1 output
targets: {1x2 cell} containing 1 target
biases: {2x1 cell} containing 2 biases
inputWeights: {2x1 cell} containing 1 input weight
layerWeights: {2x2 cell} containing 1 layer weight
functions:
adaptFcn: 'adaptwb'
initFcn: 'initlay'
performFcn: 'mse'
trainFcn: 'trainlm'
parameters:
adaptParam: .passes
initParam: (none)
performParam: (none)
trainParam: .epochs, .goal, .max_fail, .mem_reduc,
.min_grad, .mu, .mu_dec, .mu_inc,
.mu_max, .show, .time
weight and bias values:
IW: {2x1 cell} containing 1 input weight matrix
LW: {2x2 cell} containing 1 layer weight matrix
b: {2x1 cell} containing 2 bias vectors
other:
userdata: (user stuff)
Y =
Columns 1 through 4
[0.1458] [0.2512] [0.6202] [0.8446]
Columns 5 through 8
[2.1124] [2.0153] [-0.9614] [-1.0532]
Columns 9 through 10
[-2.0249] [-1.9689]
e =
Columns 1 through 4
[-0.1458] [-0.2512] [0.3798] [0.1554]
Columns 5 through 8
[-0.1124] [-0.0153] [-0.0386] [0.0532]
Columns 9 through 10
[0.0249] [-0.0311]
>> BobotAkhir_Input=net.IW{1, 1}
BobotAkhir_Input =
0.3577 0.5412
0.1812 0.1579
0.3866 -0.4315
0.3197 0.5335
0.0683 -0.0673
>> BobotAkhir_Bias_Input=net.b{1, 1}
BobotAkhir_Bias_Input =
-2.9254
1.4874
0.0651
-1.9595
3.5236
>> BobotAkhir_Lapisan=net.LW{2, 1}
BobotAkhir_Lapisan =
0.0881 0.9112 0.5924 0.5876 -0.3127
>> BobotAkhir_Bias_Lapisan=net.b{2, 1}
BobotAkhir_Bias_Lapisan =
0.2490
>> a=sim(net,p)
a =
Columns 1 through 4
[0.1456] [0.2571] [0.6469] [0.8529]
Columns 5 through 8
[2.1029] [2.0107] [-0.9545] [-1.0188]
Columns 9 through 10
[-2.0221] [-1.9857]
>> subplot(211)
3) nntwarn off
p=[7500 7500
7500 8000
6500 7750
8500 7500
5500 9000
7500 7500]
t=[0 1
1 0
0 1
1 0
1 0
0 1]
p=p/9000
p=p'
t=t'
tp=[1 5000 0.1 0.1]
[w1,b1,w2,b2]=initff(p,10,'tansig',t,'tansig')
[w1,b1,w2,b2]=trainbp(w1,b1,'tansig',w2,b2,'tansig',p,t,tp)
TRAINBP: 912/5000 epochs, SSE = 0.121904.
TRAINBP: 913/5000 epochs, SSE = 0.123948.
TRAINBP: 914/5000 epochs, SSE = 0.121815.
TRAINBP: 915/5000 epochs, SSE = 0.123858.
TRAINBP: 916/5000 epochs, SSE = 0.121727.
TRAINBP: 917/5000 epochs, SSE = 0.123767.
TRAINBP: 918/5000 epochs, SSE = 0.121638.
TRAINBP: 919/5000 epochs, SSE = 0.123677.
TRAINBP: 920/5000 epochs, SSE = 0.121551.
TRAINBP: 921/5000 epochs, SSE = 0.123587.
TRAINBP: 922/5000 epochs, SSE = 0.121463.
TRAINBP: 923/5000 epochs, SSE = 0.123497.
TRAINBP: 924/5000 epochs, SSE = 0.121376.
TRAINBP: 925/5000 epochs, SSE = 0.123408.
TRAINBP: 926/5000 epochs, SSE = 0.121289.
TRAINBP: 927/5000 epochs, SSE = 0.123319.
TRAINBP: 928/5000 epochs, SSE = 0.121202.
TRAINBP: 929/5000 epochs, SSE = 0.12323.
TRAINBP: 930/5000 epochs, SSE = 0.121116.
TRAINBP: 931/5000 epochs, SSE = 0.123142.
TRAINBP: 932/5000 epochs, SSE = 0.12103.
TRAINBP: 933/5000 epochs, SSE = 0.123054.
TRAINBP: 934/5000 epochs, SSE = 0.120944.
TRAINBP: 935/5000 epochs, SSE = 0.122966.
TRAINBP: 936/5000 epochs, SSE = 0.120859.
TRAINBP: 937/5000 epochs, SSE = 0.122879.
TRAINBP: 938/5000 epochs, SSE = 0.120774.
TRAINBP: 939/5000 epochs, SSE = 0.122792.
TRAINBP: 940/5000 epochs, SSE = 0.120689.
TRAINBP: 941/5000 epochs, SSE = 0.122705.
TRAINBP: 942/5000 epochs, SSE = 0.120605.
TRAINBP: 943/5000 epochs, SSE = 0.122619.
TRAINBP: 944/5000 epochs, SSE = 0.12052.
TRAINBP: 945/5000 epochs, SSE = 0.122532.
TRAINBP: 946/5000 epochs, SSE = 0.120437.
TRAINBP: 947/5000 epochs, SSE = 0.122447.
TRAINBP: 948/5000 epochs, SSE = 0.120353.
TRAINBP: 949/5000 epochs, SSE = 0.122361.
TRAINBP: 950/5000 epochs, SSE = 0.12027.
TRAINBP: 951/5000 epochs, SSE = 0.122276.
TRAINBP: 952/5000 epochs, SSE = 0.120187.
TRAINBP: 953/5000 epochs, SSE = 0.122191.
TRAINBP: 954/5000 epochs, SSE = 0.120104.
TRAINBP: 955/5000 epochs, SSE = 0.122106.
TRAINBP: 956/5000 epochs, SSE = 0.120021.
TRAINBP: 957/5000 epochs, SSE = 0.122022.
TRAINBP: 958/5000 epochs, SSE = 0.119939.
TRAINBP: 959/5000 epochs, SSE = 0.121937.
TRAINBP: 960/5000 epochs, SSE = 0.119857.
TRAINBP: 961/5000 epochs, SSE = 0.121854.
TRAINBP: 962/5000 epochs, SSE = 0.119776.
TRAINBP: 963/5000 epochs, SSE = 0.12177.
TRAINBP: 964/5000 epochs, SSE = 0.119694.
TRAINBP: 965/5000 epochs, SSE = 0.121687.
TRAINBP: 966/5000 epochs, SSE = 0.119613.
TRAINBP: 967/5000 epochs, SSE = 0.121604.
TRAINBP: 968/5000 epochs, SSE = 0.119532.
TRAINBP: 969/5000 epochs, SSE = 0.121521.
TRAINBP: 970/5000 epochs, SSE = 0.119452.
TRAINBP: 971/5000 epochs, SSE = 0.121438.
TRAINBP: 972/5000 epochs, SSE = 0.119372.
TRAINBP: 973/5000 epochs, SSE = 0.121356.
TRAINBP: 974/5000 epochs, SSE = 0.119292.
TRAINBP: 975/5000 epochs, SSE = 0.121274.
TRAINBP: 976/5000 epochs, SSE = 0.119212.
TRAINBP: 977/5000 epochs, SSE = 0.121193.
TRAINBP: 978/5000 epochs, SSE = 0.119132.
TRAINBP: 979/5000 epochs, SSE = 0.121111.
TRAINBP: 980/5000 epochs, SSE = 0.119053.
TRAINBP: 981/5000 epochs, SSE = 0.12103.
TRAINBP: 982/5000 epochs, SSE = 0.118974.
TRAINBP: 983/5000 epochs, SSE = 0.120949.
TRAINBP: 984/5000 epochs, SSE = 0.118895.
TRAINBP: 985/5000 epochs, SSE = 0.120868.
TRAINBP: 986/5000 epochs, SSE = 0.118817.
TRAINBP: 987/5000 epochs, SSE = 0.120788.
TRAINBP: 988/5000 epochs, SSE = 0.118739.
TRAINBP: 989/5000 epochs, SSE = 0.120708.
TRAINBP: 990/5000 epochs, SSE = 0.118661.
TRAINBP: 991/5000 epochs, SSE = 0.120628.
TRAINBP: 992/5000 epochs, SSE = 0.118583.
TRAINBP: 993/5000 epochs, SSE = 0.120548.
TRAINBP: 994/5000 epochs, SSE = 0.118505.
TRAINBP: 995/5000 epochs, SSE = 0.120469.
TRAINBP: 996/5000 epochs, SSE = 0.118428.
TRAINBP: 997/5000 epochs, SSE = 0.12039.
TRAINBP: 998/5000 epochs, SSE = 0.118351.
TRAINBP: 999/5000 epochs, SSE = 0.120311.
TRAINBP: 1000/5000 epochs, SSE = 0.118274.
TRAINBP: 1001/5000 epochs, SSE = 0.120232.
TRAINBP: 1002/5000 epochs, SSE = 0.118198.
TRAINBP: 1003/5000 epochs, SSE = 0.120154.
TRAINBP: 1004/5000 epochs, SSE = 0.118121.
TRAINBP: 1005/5000 epochs, SSE = 0.120075.
TRAINBP: 1006/5000 epochs, SSE = 0.118045.
TRAINBP: 1007/5000 epochs, SSE = 0.119997.
TRAINBP: 1008/5000 epochs, SSE = 0.117969.
TRAINBP: 1009/5000 epochs, SSE = 0.11992.
TRAINBP: 1010/5000 epochs, SSE = 0.117894.
TRAINBP: 1011/5000 epochs, SSE = 0.119842.
TRAINBP: 1012/5000 epochs, SSE = 0.117818.
TRAINBP: 1013/5000 epochs, SSE = 0.119765.
TRAINBP: 1014/5000 epochs, SSE = 0.117743.
TRAINBP: 1015/5000 epochs, SSE = 0.119688.
TRAINBP: 1016/5000 epochs, SSE = 0.117668.
TRAINBP: 1017/5000 epochs, SSE = 0.119611.
TRAINBP: 1018/5000 epochs, SSE = 0.117593.
TRAINBP: 1019/5000 epochs, SSE = 0.119534.
TRAINBP: 1020/5000 epochs, SSE = 0.117519.
TRAINBP: 1021/5000 epochs, SSE = 0.119458.
TRAINBP: 1022/5000 epochs, SSE = 0.117445.
TRAINBP: 1023/5000 epochs, SSE = 0.119382.
TRAINBP: 1024/5000 epochs, SSE = 0.11737.
TRAINBP: 1025/5000 epochs, SSE = 0.119306.
TRAINBP: 1026/5000 epochs, SSE = 0.117297.
TRAINBP: 1027/5000 epochs, SSE = 0.11923.
TRAINBP: 1028/5000 epochs, SSE = 0.117223.
TRAINBP: 1029/5000 epochs, SSE = 0.119155.
TRAINBP: 1030/5000 epochs, SSE = 0.117149.
TRAINBP: 1031/5000 epochs, SSE = 0.119079.
TRAINBP: 1032/5000 epochs, SSE = 0.117076.
TRAINBP: 1033/5000 epochs, SSE = 0.119004.
TRAINBP: 1034/5000 epochs, SSE = 0.117003.
TRAINBP: 1035/5000 epochs, SSE = 0.118929.
TRAINBP: 1036/5000 epochs, SSE = 0.11693.
TRAINBP: 1037/5000 epochs, SSE = 0.118855.
TRAINBP: 1038/5000 epochs, SSE = 0.116858.
TRAINBP: 1039/5000 epochs, SSE = 0.11878.
TRAINBP: 1040/5000 epochs, SSE = 0.116785.
TRAINBP: 1041/5000 epochs, SSE = 0.118706.
TRAINBP: 1042/5000 epochs, SSE = 0.116713.
TRAINBP: 1043/5000 epochs, SSE = 0.118632.
TRAINBP: 1044/5000 epochs, SSE = 0.116641.
TRAINBP: 1045/5000 epochs, SSE = 0.118558.
TRAINBP: 1046/5000 epochs, SSE = 0.116569.
TRAINBP: 1047/5000 epochs, SSE = 0.118485.
TRAINBP: 1048/5000 epochs, SSE = 0.116498.
TRAINBP: 1049/5000 epochs, SSE = 0.118411.
TRAINBP: 1050/5000 epochs, SSE = 0.116426.
TRAINBP: 1051/5000 epochs, SSE = 0.118338.
TRAINBP: 1052/5000 epochs, SSE = 0.116355.
TRAINBP: 1053/5000 epochs, SSE = 0.118265.
TRAINBP: 1054/5000 epochs, SSE = 0.116284.
TRAINBP: 1055/5000 epochs, SSE = 0.118192.
TRAINBP: 1056/5000 epochs, SSE = 0.116213.
TRAINBP: 1057/5000 epochs, SSE = 0.11812.
TRAINBP: 1058/5000 epochs, SSE = 0.116143.
TRAINBP: 1059/5000 epochs, SSE = 0.118047.
TRAINBP: 1060/5000 epochs, SSE = 0.116072.
TRAINBP: 1061/5000 epochs, SSE = 0.117975.
TRAINBP: 1062/5000 epochs, SSE = 0.116002.
TRAINBP: 1063/5000 epochs, SSE = 0.117903.
TRAINBP: 1064/5000 epochs, SSE = 0.115932.
TRAINBP: 1065/5000 epochs, SSE = 0.117831.
TRAINBP: 1066/5000 epochs, SSE = 0.115862.
TRAINBP: 1067/5000 epochs, SSE = 0.117759.
TRAINBP: 1068/5000 epochs, SSE = 0.115792.
TRAINBP: 1069/5000 epochs, SSE = 0.117688.
TRAINBP: 1070/5000 epochs, SSE = 0.115723.
TRAINBP: 1071/5000 epochs, SSE = 0.117616.
TRAINBP: 1072/5000 epochs, SSE = 0.115653.
TRAINBP: 1073/5000 epochs, SSE = 0.117545.
TRAINBP: 1074/5000 epochs, SSE = 0.115584.
TRAINBP: 1075/5000 epochs, SSE = 0.117474.
TRAINBP: 1076/5000 epochs, SSE = 0.115515.
TRAINBP: 1077/5000 epochs, SSE = 0.117404.
TRAINBP: 1078/5000 epochs, SSE = 0.115446.
TRAINBP: 1079/5000 epochs, SSE = 0.117333.
TRAINBP: 1080/5000 epochs, SSE = 0.115378.
TRAINBP: 1081/5000 epochs, SSE = 0.117263.
TRAINBP: 1082/5000 epochs, SSE = 0.115309.
TRAINBP: 1083/5000 epochs, SSE = 0.117192.
TRAINBP: 1084/5000 epochs, SSE = 0.115241.
TRAINBP: 1085/5000 epochs, SSE = 0.117122.
TRAINBP: 1086/5000 epochs, SSE = 0.115173.
TRAINBP: 1087/5000 epochs, SSE = 0.117052.
TRAINBP: 1088/5000 epochs, SSE = 0.115105.
TRAINBP: 1089/5000 epochs, SSE = 0.116983.
TRAINBP: 1090/5000 epochs, SSE = 0.115037.
TRAINBP: 1091/5000 epochs, SSE = 0.116913.
TRAINBP: 1092/5000 epochs, SSE = 0.114969.
TRAINBP: 1093/5000 epochs, SSE = 0.116844.
TRAINBP: 1094/5000 epochs, SSE = 0.114902.
TRAINBP: 1095/5000 epochs, SSE = 0.116775.
TRAINBP: 1096/5000 epochs, SSE = 0.114835.
TRAINBP: 1097/5000 epochs, SSE = 0.116706.
TRAINBP: 1098/5000 epochs, SSE = 0.114768.
TRAINBP: 1099/5000 epochs, SSE = 0.116637.
TRAINBP: 1100/5000 epochs, SSE = 0.114701.
TRAINBP: 1101/5000 epochs, SSE = 0.116568.
TRAINBP: 1102/5000 epochs, SSE = 0.114634.
TRAINBP: 1103/5000 epochs, SSE = 0.1165.
TRAINBP: 1104/5000 epochs, SSE = 0.114567.
TRAINBP: 1105/5000 epochs, SSE = 0.116431.
TRAINBP: 1106/5000 epochs, SSE = 0.114501.
TRAINBP: 1107/5000 epochs, SSE = 0.116363.
TRAINBP: 1108/5000 epochs, SSE = 0.114435.
TRAINBP: 1109/5000 epochs, SSE = 0.116295.
TRAINBP: 1110/5000 epochs, SSE = 0.114368.
TRAINBP: 1111/5000 epochs, SSE = 0.116227.
TRAINBP: 1112/5000 epochs, SSE = 0.114302.
TRAINBP: 1113/5000 epochs, SSE = 0.11616.
TRAINBP: 1114/5000 epochs, SSE = 0.114237.
TRAINBP: 1115/5000 epochs, SSE = 0.116092.
TRAINBP: 1116/5000 epochs, SSE = 0.114171.
TRAINBP: 1117/5000 epochs, SSE = 0.116025.
TRAINBP: 1118/5000 epochs, SSE = 0.114105.
TRAINBP: 1119/5000 epochs, SSE = 0.115957.
TRAINBP: 1120/5000 epochs, SSE = 0.11404.
TRAINBP: 1121/5000 epochs, SSE = 0.11589.
TRAINBP: 1122/5000 epochs, SSE = 0.113975.
TRAINBP: 1123/5000 epochs, SSE = 0.115823.
TRAINBP: 1124/5000 epochs, SSE = 0.11391.
TRAINBP: 1125/5000 epochs, SSE = 0.115757.
TRAINBP: 1126/5000 epochs, SSE = 0.113845.
TRAINBP: 1127/5000 epochs, SSE = 0.11569.
TRAINBP: 1128/5000 epochs, SSE = 0.11378.
TRAINBP: 1129/5000 epochs, SSE = 0.115624.
TRAINBP: 1130/5000 epochs, SSE = 0.113716.
TRAINBP: 1131/5000 epochs, SSE = 0.115557.
TRAINBP: 1132/5000 epochs, SSE = 0.113651.
TRAINBP: 1133/5000 epochs, SSE = 0.115491.
TRAINBP: 1134/5000 epochs, SSE = 0.113587.
TRAINBP: 1135/5000 epochs, SSE = 0.115425.
TRAINBP: 1136/5000 epochs, SSE = 0.113523.
TRAINBP: 1137/5000 epochs, SSE = 0.115359.
TRAINBP: 1138/5000 epochs, SSE = 0.113459.
TRAINBP: 1139/5000 epochs, SSE = 0.115294.
TRAINBP: 1140/5000 epochs, SSE = 0.113395.
TRAINBP: 1141/5000 epochs, SSE = 0.115228.
TRAINBP: 1142/5000 epochs, SSE = 0.113331.
TRAINBP: 1143/5000 epochs, SSE = 0.115163.
TRAINBP: 1144/5000 epochs, SSE = 0.113267.
TRAINBP: 1145/5000 epochs, SSE = 0.115097.
TRAINBP: 1146/5000 epochs, SSE = 0.113204.
TRAINBP: 1147/5000 epochs, SSE = 0.115032.
TRAINBP: 1148/5000 epochs, SSE = 0.113141.
TRAINBP: 1149/5000 epochs, SSE = 0.114967.
TRAINBP: 1150/5000 epochs, SSE = 0.113077.
TRAINBP: 1151/5000 epochs, SSE = 0.114902.
TRAINBP: 1152/5000 epochs, SSE = 0.113014.
TRAINBP: 1153/5000 epochs, SSE = 0.114838.
TRAINBP: 1154/5000 epochs, SSE = 0.112951.
TRAINBP: 1155/5000 epochs, SSE = 0.114773.
TRAINBP: 1156/5000 epochs, SSE = 0.112889.
TRAINBP: 1157/5000 epochs, SSE = 0.114708.
TRAINBP: 1158/5000 epochs, SSE = 0.112826.
TRAINBP: 1159/5000 epochs, SSE = 0.114644.
TRAINBP: 1160/5000 epochs, SSE = 0.112764.
TRAINBP: 1161/5000 epochs, SSE = 0.11458.
TRAINBP: 1162/5000 epochs, SSE = 0.112701.
TRAINBP: 1163/5000 epochs, SSE = 0.114516.
TRAINBP: 1164/5000 epochs, SSE = 0.112639.
TRAINBP: 1165/5000 epochs, SSE = 0.114452.
TRAINBP: 1166/5000 epochs, SSE = 0.112577.
TRAINBP: 1167/5000 epochs, SSE = 0.114388.
TRAINBP: 1168/5000 epochs, SSE = 0.112515.
TRAINBP: 1169/5000 epochs, SSE = 0.114325.
TRAINBP: 1170/5000 epochs, SSE = 0.112453.
TRAINBP: 1171/5000 epochs, SSE = 0.114261.
TRAINBP: 1172/5000 epochs, SSE = 0.112391.
TRAINBP: 1173/5000 epochs, SSE = 0.114198.
TRAINBP: 1174/5000 epochs, SSE = 0.11233.
TRAINBP: 1175/5000 epochs, SSE = 0.114134.
TRAINBP: 1176/5000 epochs, SSE = 0.112268.
TRAINBP: 1177/5000 epochs, SSE = 0.114071.
TRAINBP: 1178/5000 epochs, SSE = 0.112207.
TRAINBP: 1179/5000 epochs, SSE = 0.114008.
TRAINBP: 1180/5000 epochs, SSE = 0.112146.
TRAINBP: 1181/5000 epochs, SSE = 0.113945.
TRAINBP: 1182/5000 epochs, SSE = 0.112084.
TRAINBP: 1183/5000 epochs, SSE = 0.113883.
TRAINBP: 1184/5000 epochs, SSE = 0.112023.
TRAINBP: 1185/5000 epochs, SSE = 0.11382.
TRAINBP: 1186/5000 epochs, SSE = 0.111963.
TRAINBP: 1187/5000 epochs, SSE = 0.113758.
TRAINBP: 1188/5000 epochs, SSE = 0.111902.
TRAINBP: 1189/5000 epochs, SSE = 0.113695.
TRAINBP: 1190/5000 epochs, SSE = 0.111841.
TRAINBP: 1191/5000 epochs, SSE = 0.113633.
TRAINBP: 1192/5000 epochs, SSE = 0.111781.
TRAINBP: 1193/5000 epochs, SSE = 0.113571.
TRAINBP: 1194/5000 epochs, SSE = 0.11172.
TRAINBP: 1195/5000 epochs, SSE = 0.113509.
TRAINBP: 1196/5000 epochs, SSE = 0.11166.
TRAINBP: 1197/5000 epochs, SSE = 0.113447.
TRAINBP: 1198/5000 epochs, SSE = 0.1116.
TRAINBP: 1199/5000 epochs, SSE = 0.113385.
TRAINBP: 1200/5000 epochs, SSE = 0.11154.
TRAINBP: 1201/5000 epochs, SSE = 0.113323.
TRAINBP: 1202/5000 epochs, SSE = 0.11148.
TRAINBP: 1203/5000 epochs, SSE = 0.113262.
TRAINBP: 1204/5000 epochs, SSE = 0.11142.
TRAINBP: 1205/5000 epochs, SSE = 0.1132.
TRAINBP: 1206/5000 epochs, SSE = 0.111361.
TRAINBP: 1207/5000 epochs, SSE = 0.113139.
TRAINBP: 1208/5000 epochs, SSE = 0.111301.
TRAINBP: 1209/5000 epochs, SSE = 0.113078.
TRAINBP: 1210/5000 epochs, SSE = 0.111241.
TRAINBP: 1211/5000 epochs, SSE = 0.113017.
TRAINBP: 1212/5000 epochs, SSE = 0.111182.
TRAINBP: 1213/5000 epochs, SSE = 0.112956.
TRAINBP: 1214/5000 epochs, SSE = 0.111123.
TRAINBP: 1215/5000 epochs, SSE = 0.112895.
TRAINBP: 1216/5000 epochs, SSE = 0.111064.
TRAINBP: 1217/5000 epochs, SSE = 0.112834.
TRAINBP: 1218/5000 epochs, SSE = 0.111005.
TRAINBP: 1219/5000 epochs, SSE = 0.112774.
TRAINBP: 1220/5000 epochs, SSE = 0.110946.
TRAINBP: 1221/5000 epochs, SSE = 0.112713.
TRAINBP: 1222/5000 epochs, SSE = 0.110887.
TRAINBP: 1223/5000 epochs, SSE = 0.112653.
TRAINBP: 1224/5000 epochs, SSE = 0.110828.
TRAINBP: 1225/5000 epochs, SSE = 0.112592.
TRAINBP: 1226/5000 epochs, SSE = 0.11077.
TRAINBP: 1227/5000 epochs, SSE = 0.112532.
TRAINBP: 1228/5000 epochs, SSE = 0.110711.
TRAINBP: 1229/5000 epochs, SSE = 0.112472.
TRAINBP: 1230/5000 epochs, SSE = 0.110653.
TRAINBP: 1231/5000 epochs, SSE = 0.112412.
TRAINBP: 1232/5000 epochs, SSE = 0.110595.
TRAINBP: 1233/5000 epochs, SSE = 0.112352.
TRAINBP: 1234/5000 epochs, SSE = 0.110536.
TRAINBP: 1235/5000 epochs, SSE = 0.112292.
TRAINBP: 1236/5000 epochs, SSE = 0.110478.
TRAINBP: 1237/5000 epochs, SSE = 0.112233.
TRAINBP: 1238/5000 epochs, SSE = 0.11042.
TRAINBP: 1239/5000 epochs, SSE = 0.112173.
TRAINBP: 1240/5000 epochs, SSE = 0.110363.
TRAINBP: 1241/5000 epochs, SSE = 0.112114.
TRAINBP: 1242/5000 epochs, SSE = 0.110305.
TRAINBP: 1243/5000 epochs, SSE = 0.112054.
TRAINBP: 1244/5000 epochs, SSE = 0.110247.
TRAINBP: 1245/5000 epochs, SSE = 0.111995.
TRAINBP: 1246/5000 epochs, SSE = 0.11019.
TRAINBP: 1247/5000 epochs, SSE = 0.111936.
TRAINBP: 1248/5000 epochs, SSE = 0.110132.
TRAINBP: 1249/5000 epochs, SSE = 0.111877.
TRAINBP: 1250/5000 epochs, SSE = 0.110075.
TRAINBP: 1251/5000 epochs, SSE = 0.111818.
TRAINBP: 1252/5000 epochs, SSE = 0.110017.
TRAINBP: 1253/5000 epochs, SSE = 0.111759.
TRAINBP: 1254/5000 epochs, SSE = 0.10996.
TRAINBP: 1255/5000 epochs, SSE = 0.1117.
TRAINBP: 1256/5000 epochs, SSE = 0.109903.
TRAINBP: 1257/5000 epochs, SSE = 0.111642.
TRAINBP: 1258/5000 epochs, SSE = 0.109846.
TRAINBP: 1259/5000 epochs, SSE = 0.111583.
TRAINBP: 1260/5000 epochs, SSE = 0.109789.
TRAINBP: 1261/5000 epochs, SSE = 0.111525.
TRAINBP: 1262/5000 epochs, SSE = 0.109732.
TRAINBP: 1263/5000 epochs, SSE = 0.111466.
TRAINBP: 1264/5000 epochs, SSE = 0.109676.
TRAINBP: 1265/5000 epochs, SSE = 0.111408.
TRAINBP: 1266/5000 epochs, SSE = 0.109619.
TRAINBP: 1267/5000 epochs, SSE = 0.11135.
TRAINBP: 1268/5000 epochs, SSE = 0.109563.
TRAINBP: 1269/5000 epochs, SSE = 0.111292.
TRAINBP: 1270/5000 epochs, SSE = 0.109506.
TRAINBP: 1271/5000 epochs, SSE = 0.111234.
TRAINBP: 1272/5000 epochs, SSE = 0.10945.
TRAINBP: 1273/5000 epochs, SSE = 0.111176.
TRAINBP: 1274/5000 epochs, SSE = 0.109394.
TRAINBP: 1275/5000 epochs, SSE = 0.111118.
TRAINBP: 1276/5000 epochs, SSE = 0.109338.
TRAINBP: 1277/5000 epochs, SSE = 0.11106.
TRAINBP: 1278/5000 epochs, SSE = 0.109281.
TRAINBP: 1279/5000 epochs, SSE = 0.111003.
TRAINBP: 1280/5000 epochs, SSE = 0.109225.
TRAINBP: 1281/5000 epochs, SSE = 0.110945.
TRAINBP: 1282/5000 epochs, SSE = 0.10917.
TRAINBP: 1283/5000 epochs, SSE = 0.110888.
TRAINBP: 1284/5000 epochs, SSE = 0.109114.
TRAINBP: 1285/5000 epochs, SSE = 0.11083.
TRAINBP: 1286/5000 epochs, SSE = 0.109058.
TRAINBP: 1287/5000 epochs, SSE = 0.110773.
TRAINBP: 1288/5000 epochs, SSE = 0.109002.
TRAINBP: 1289/5000 epochs, SSE = 0.110716.
TRAINBP: 1290/5000 epochs, SSE = 0.108947.
TRAINBP: 1291/5000 epochs, SSE = 0.110659.
TRAINBP: 1292/5000 epochs, SSE = 0.108891.
TRAINBP: 1293/5000 epochs, SSE = 0.110602.
TRAINBP: 1294/5000 epochs, SSE = 0.108836.
TRAINBP: 1295/5000 epochs, SSE = 0.110545.
TRAINBP: 1296/5000 epochs, SSE = 0.108781.
TRAINBP: 1297/5000 epochs, SSE = 0.110488.
TRAINBP: 1298/5000 epochs, SSE = 0.108726.
TRAINBP: 1299/5000 epochs, SSE = 0.110431.
TRAINBP: 1300/5000 epochs, SSE = 0.108671.
TRAINBP: 1301/5000 epochs, SSE = 0.110375.
TRAINBP: 1302/5000 epochs, SSE = 0.108615.
TRAINBP: 1303/5000 epochs, SSE = 0.110318.
TRAINBP: 1304/5000 epochs, SSE = 0.108561.
TRAINBP: 1305/5000 epochs, SSE = 0.110262.
TRAINBP: 1306/5000 epochs, SSE = 0.108506.
TRAINBP: 1307/5000 epochs, SSE = 0.110205.
TRAINBP: 1308/5000 epochs, SSE = 0.108451.
TRAINBP: 1309/5000 epochs, SSE = 0.110149.
TRAINBP: 1310/5000 epochs, SSE = 0.108396.
TRAINBP: 1311/5000 epochs, SSE = 0.110093.
TRAINBP: 1312/5000 epochs, SSE = 0.108342.
TRAINBP: 1313/5000 epochs, SSE = 0.110036.
TRAINBP: 1314/5000 epochs, SSE = 0.108287.
TRAINBP: 1315/5000 epochs, SSE = 0.10998.
TRAINBP: 1316/5000 epochs, SSE = 0.108232.
TRAINBP: 1317/5000 epochs, SSE = 0.109924.
TRAINBP: 1318/5000 epochs, SSE = 0.108178.
TRAINBP: 1319/5000 epochs, SSE = 0.109868.
TRAINBP: 1320/5000 epochs, SSE = 0.108124.
TRAINBP: 1321/5000 epochs, SSE = 0.109812.
TRAINBP: 1322/5000 epochs, SSE = 0.10807.
TRAINBP: 1323/5000 epochs, SSE = 0.109757.
TRAINBP: 1324/5000 epochs, SSE = 0.108015.
TRAINBP: 1325/5000 epochs, SSE = 0.109701.
TRAINBP: 1326/5000 epochs, SSE = 0.107961.
TRAINBP: 1327/5000 epochs, SSE = 0.109645.
TRAINBP: 1328/5000 epochs, SSE = 0.107907.
TRAINBP: 1329/5000 epochs, SSE = 0.10959.
TRAINBP: 1330/5000 epochs, SSE = 0.107853.
TRAINBP: 1331/5000 epochs, SSE = 0.109534.
TRAINBP: 1332/5000 epochs, SSE = 0.107799.
TRAINBP: 1333/5000 epochs, SSE = 0.109479.
TRAINBP: 1334/5000 epochs, SSE = 0.107746.
TRAINBP: 1335/5000 epochs, SSE = 0.109424.
TRAINBP: 1336/5000 epochs, SSE = 0.107692.
TRAINBP: 1337/5000 epochs, SSE = 0.109368.
TRAINBP: 1338/5000 epochs, SSE = 0.107638.
TRAINBP: 1339/5000 epochs, SSE = 0.109313.
TRAINBP: 1340/5000 epochs, SSE = 0.107585.
TRAINBP: 1341/5000 epochs, SSE = 0.109258.
TRAINBP: 1342/5000 epochs, SSE = 0.107531.
TRAINBP: 1343/5000 epochs, SSE = 0.109203.
TRAINBP: 1344/5000 epochs, SSE = 0.107478.
TRAINBP: 1345/5000 epochs, SSE = 0.109148.
TRAINBP: 1346/5000 epochs, SSE = 0.107425.
TRAINBP: 1347/5000 epochs, SSE = 0.109093.
TRAINBP: 1348/5000 epochs, SSE = 0.107371.
TRAINBP: 1349/5000 epochs, SSE = 0.109039.
TRAINBP: 1350/5000 epochs, SSE = 0.107318.
TRAINBP: 1351/5000 epochs, SSE = 0.108984.
TRAINBP: 1352/5000 epochs, SSE = 0.107265.
TRAINBP: 1353/5000 epochs, SSE = 0.108929.
TRAINBP: 1354/5000 epochs, SSE = 0.107212.
TRAINBP: 1355/5000 epochs, SSE = 0.108875.
TRAINBP: 1356/5000 epochs, SSE = 0.107159.
TRAINBP: 1357/5000 epochs, SSE = 0.10882.
TRAINBP: 1358/5000 epochs, SSE = 0.107106.
TRAINBP: 1359/5000 epochs, SSE = 0.108766.
TRAINBP: 1360/5000 epochs, SSE = 0.107053.
TRAINBP: 1361/5000 epochs, SSE = 0.108711.
TRAINBP: 1362/5000 epochs, SSE = 0.107.
TRAINBP: 1363/5000 epochs, SSE = 0.108657.
TRAINBP: 1364/5000 epochs, SSE = 0.106947.
TRAINBP: 1365/5000 epochs, SSE = 0.108603.
TRAINBP: 1366/5000 epochs, SSE = 0.106895.
TRAINBP: 1367/5000 epochs, SSE = 0.108549.
TRAINBP: 1368/5000 epochs, SSE = 0.106842.
TRAINBP: 1369/5000 epochs, SSE = 0.108494.
TRAINBP: 1370/5000 epochs, SSE = 0.10679.
TRAINBP: 1371/5000 epochs, SSE = 0.10844.
TRAINBP: 1372/5000 epochs, SSE = 0.106737.
TRAINBP: 1373/5000 epochs, SSE = 0.108386.
TRAINBP: 1374/5000 epochs, SSE = 0.106685.
TRAINBP: 1375/5000 epochs, SSE = 0.108332.
TRAINBP: 1376/5000 epochs, SSE = 0.106632.
TRAINBP: 1377/5000 epochs, SSE = 0.108279.
TRAINBP: 1378/5000 epochs, SSE = 0.10658.
TRAINBP: 1379/5000 epochs, SSE = 0.108225.
TRAINBP: 1380/5000 epochs, SSE = 0.106528.
TRAINBP: 1381/5000 epochs, SSE = 0.108171.
TRAINBP: 1382/5000 epochs, SSE = 0.106476.
TRAINBP: 1383/5000 epochs, SSE = 0.108118.
TRAINBP: 1384/5000 epochs, SSE = 0.106424.
TRAINBP: 1385/5000 epochs, SSE = 0.108064.
TRAINBP: 1386/5000 epochs, SSE = 0.106372.
TRAINBP: 1387/5000 epochs, SSE = 0.10801.
TRAINBP: 1388/5000 epochs, SSE = 0.10632.
TRAINBP: 1389/5000 epochs, SSE = 0.107957.
TRAINBP: 1390/5000 epochs, SSE = 0.106268.
TRAINBP: 1391/5000 epochs, SSE = 0.107904.
TRAINBP: 1392/5000 epochs, SSE = 0.106216.
TRAINBP: 1393/5000 epochs, SSE = 0.10785.
TRAINBP: 1394/5000 epochs, SSE = 0.106164.
TRAINBP: 1395/5000 epochs, SSE = 0.107797.
TRAINBP: 1396/5000 epochs, SSE = 0.106112.
TRAINBP: 1397/5000 epochs, SSE = 0.107744.
TRAINBP: 1398/5000 epochs, SSE = 0.106061.
TRAINBP: 1399/5000 epochs, SSE = 0.107691.
TRAINBP: 1400/5000 epochs, SSE = 0.106009.
TRAINBP: 1401/5000 epochs, SSE = 0.107638.
TRAINBP: 1402/5000 epochs, SSE = 0.105958.
TRAINBP: 1403/5000 epochs, SSE = 0.107584.
TRAINBP: 1404/5000 epochs, SSE = 0.105906.
TRAINBP: 1405/5000 epochs, SSE = 0.107532.
TRAINBP: 1406/5000 epochs, SSE = 0.105855.
TRAINBP: 1407/5000 epochs, SSE = 0.107479.
TRAINBP: 1408/5000 epochs, SSE = 0.105803.
TRAINBP: 1409/5000 epochs, SSE = 0.107426.
TRAINBP: 1410/5000 epochs, SSE = 0.105752.
TRAINBP: 1411/5000 epochs, SSE = 0.107373.
TRAINBP: 1412/5000 epochs, SSE = 0.105701.
TRAINBP: 1413/5000 epochs, SSE = 0.10732.
TRAINBP: 1414/5000 epochs, SSE = 0.10565.
TRAINBP: 1415/5000 epochs, SSE = 0.107268.
TRAINBP: 1416/5000 epochs, SSE = 0.105599.
TRAINBP: 1417/5000 epochs, SSE = 0.107215.
TRAINBP: 1418/5000 epochs, SSE = 0.105547.
TRAINBP: 1419/5000 epochs, SSE = 0.107162.
TRAINBP: 1420/5000 epochs, SSE = 0.105496.
TRAINBP: 1421/5000 epochs, SSE = 0.10711.
TRAINBP: 1422/5000 epochs, SSE = 0.105445.
TRAINBP: 1423/5000 epochs, SSE = 0.107057.
TRAINBP: 1424/5000 epochs, SSE = 0.105395.
TRAINBP: 1425/5000 epochs, SSE = 0.107005.
TRAINBP: 1426/5000 epochs, SSE = 0.105344.
TRAINBP: 1427/5000 epochs, SSE = 0.106953.
TRAINBP: 1428/5000 epochs, SSE = 0.105293.
TRAINBP: 1429/5000 epochs, SSE = 0.1069.
TRAINBP: 1430/5000 epochs, SSE = 0.105242.
TRAINBP: 1431/5000 epochs, SSE = 0.106848.
TRAINBP: 1432/5000 epochs, SSE = 0.105191.
TRAINBP: 1433/5000 epochs, SSE = 0.106796.
TRAINBP: 1434/5000 epochs, SSE = 0.105141.
TRAINBP: 1435/5000 epochs, SSE = 0.106744.
TRAINBP: 1436/5000 epochs, SSE = 0.10509.
TRAINBP: 1437/5000 epochs, SSE = 0.106692.
TRAINBP: 1438/5000 epochs, SSE = 0.10504.
TRAINBP: 1439/5000 epochs, SSE = 0.10664.
TRAINBP: 1440/5000 epochs, SSE = 0.104989.
TRAINBP: 1441/5000 epochs, SSE = 0.106588.
TRAINBP: 1442/5000 epochs, SSE = 0.104939.
TRAINBP: 1443/5000 epochs, SSE = 0.106536.
TRAINBP: 1444/5000 epochs, SSE = 0.104888.
TRAINBP: 1445/5000 epochs, SSE = 0.106484.
TRAINBP: 1446/5000 epochs, SSE = 0.104838.
TRAINBP: 1447/5000 epochs, SSE = 0.106432.
TRAINBP: 1448/5000 epochs, SSE = 0.104788.
TRAINBP: 1449/5000 epochs, SSE = 0.106381.
TRAINBP: 1450/5000 epochs, SSE = 0.104737.
TRAINBP: 1451/5000 epochs, SSE = 0.106329.
TRAINBP: 1452/5000 epochs, SSE = 0.104687.
TRAINBP: 1453/5000 epochs, SSE = 0.106277.
TRAINBP: 1454/5000 epochs, SSE = 0.104637.
TRAINBP: 1455/5000 epochs, SSE = 0.106226.
TRAINBP: 1456/5000 epochs, SSE = 0.104587.
TRAINBP: 1457/5000 epochs, SSE = 0.106174.
TRAINBP: 1458/5000 epochs, SSE = 0.104537.
TRAINBP: 1459/5000 epochs, SSE = 0.106123.
TRAINBP: 1460/5000 epochs, SSE = 0.104487.
TRAINBP: 1461/5000 epochs, SSE = 0.106071.
TRAINBP: 1462/5000 epochs, SSE = 0.104437.
TRAINBP: 1463/5000 epochs, SSE = 0.10602.
TRAINBP: 1464/5000 epochs, SSE = 0.104387.
TRAINBP: 1465/5000 epochs, SSE = 0.105968.
TRAINBP: 1466/5000 epochs, SSE = 0.104337.
TRAINBP: 1467/5000 epochs, SSE = 0.105917.
TRAINBP: 1468/5000 epochs, SSE = 0.104287.
TRAINBP: 1469/5000 epochs, SSE = 0.105866.
TRAINBP: 1470/5000 epochs, SSE = 0.104238.
TRAINBP: 1471/5000 epochs, SSE = 0.105815.
TRAINBP: 1472/5000 epochs, SSE = 0.104188.
TRAINBP: 1473/5000 epochs, SSE = 0.105763.
TRAINBP: 1474/5000 epochs, SSE = 0.104138.
TRAINBP: 1475/5000 epochs, SSE = 0.105712.
TRAINBP: 1476/5000 epochs, SSE = 0.104089.
TRAINBP: 1477/5000 epochs, SSE = 0.105661.
TRAINBP: 1478/5000 epochs, SSE = 0.104039.
TRAINBP: 1479/5000 epochs, SSE = 0.10561.
TRAINBP: 1480/5000 epochs, SSE = 0.10399.
TRAINBP: 1481/5000 epochs, SSE = 0.105559.
TRAINBP: 1482/5000 epochs, SSE = 0.10394.
TRAINBP: 1483/5000 epochs, SSE = 0.105508.
TRAINBP: 1484/5000 epochs, SSE = 0.103891.
TRAINBP: 1485/5000 epochs, SSE = 0.105458.
TRAINBP: 1486/5000 epochs, SSE = 0.103841.
TRAINBP: 1487/5000 epochs, SSE = 0.105407.
TRAINBP: 1488/5000 epochs, SSE = 0.103792.
TRAINBP: 1489/5000 epochs, SSE = 0.105356.
TRAINBP: 1490/5000 epochs, SSE = 0.103743.
TRAINBP: 1491/5000 epochs, SSE = 0.105305.
TRAINBP: 1492/5000 epochs, SSE = 0.103693.
TRAINBP: 1493/5000 epochs, SSE = 0.105254.
TRAINBP: 1494/5000 epochs, SSE = 0.103644.
TRAINBP: 1495/5000 epochs, SSE = 0.105204.
TRAINBP: 1496/5000 epochs, SSE = 0.103595.
TRAINBP: 1497/5000 epochs, SSE = 0.105153.
TRAINBP: 1498/5000 epochs, SSE = 0.103546.
TRAINBP: 1499/5000 epochs, SSE = 0.105103.
TRAINBP: 1500/5000 epochs, SSE = 0.103497.
TRAINBP: 1501/5000 epochs, SSE = 0.105052.
TRAINBP: 1502/5000 epochs, SSE = 0.103448.
TRAINBP: 1503/5000 epochs, SSE = 0.105002.
TRAINBP: 1504/5000 epochs, SSE = 0.103399.
TRAINBP: 1505/5000 epochs, SSE = 0.104951.
TRAINBP: 1506/5000 epochs, SSE = 0.10335.
TRAINBP: 1507/5000 epochs, SSE = 0.104901.
TRAINBP: 1508/5000 epochs, SSE = 0.103301.
TRAINBP: 1509/5000 epochs, SSE = 0.10485.
TRAINBP: 1510/5000 epochs, SSE = 0.103252.
TRAINBP: 1511/5000 epochs, SSE = 0.1048.
TRAINBP: 1512/5000 epochs, SSE = 0.103203.
TRAINBP: 1513/5000 epochs, SSE = 0.10475.
TRAINBP: 1514/5000 epochs, SSE = 0.103154.
TRAINBP: 1515/5000 epochs, SSE = 0.1047.
TRAINBP: 1516/5000 epochs, SSE = 0.103106.
TRAINBP: 1517/5000 epochs, SSE = 0.104649.
TRAINBP: 1518/5000 epochs, SSE = 0.103057.
TRAINBP: 1519/5000 epochs, SSE = 0.104599.
TRAINBP: 1520/5000 epochs, SSE = 0.103008.
TRAINBP: 1521/5000 epochs, SSE = 0.104549.
TRAINBP: 1522/5000 epochs, SSE = 0.10296.
TRAINBP: 1523/5000 epochs, SSE = 0.104499.
TRAINBP: 1524/5000 epochs, SSE = 0.102911.
TRAINBP: 1525/5000 epochs, SSE = 0.104449.
TRAINBP: 1526/5000 epochs, SSE = 0.102862.
TRAINBP: 1527/5000 epochs, SSE = 0.104399.
TRAINBP: 1528/5000 epochs, SSE = 0.102814.
TRAINBP: 1529/5000 epochs, SSE = 0.104349.
TRAINBP: 1530/5000 epochs, SSE = 0.102765.
TRAINBP: 1531/5000 epochs, SSE = 0.104299.
TRAINBP: 1532/5000 epochs, SSE = 0.102717.
TRAINBP: 1533/5000 epochs, SSE = 0.104249.
TRAINBP: 1534/5000 epochs, SSE = 0.102668.
TRAINBP: 1535/5000 epochs, SSE = 0.104199.
TRAINBP: 1536/5000 epochs, SSE = 0.10262.
TRAINBP: 1537/5000 epochs, SSE = 0.10415.
TRAINBP: 1538/5000 epochs, SSE = 0.102572.
TRAINBP: 1539/5000 epochs, SSE = 0.1041.
TRAINBP: 1540/5000 epochs, SSE = 0.102523.
TRAINBP: 1541/5000 epochs, SSE = 0.10405.
TRAINBP: 1542/5000 epochs, SSE = 0.102475.
TRAINBP: 1543/5000 epochs, SSE = 0.104.
TRAINBP: 1544/5000 epochs, SSE = 0.102427.
TRAINBP: 1545/5000 epochs, SSE = 0.103951.
TRAINBP: 1546/5000 epochs, SSE = 0.102379.
TRAINBP: 1547/5000 epochs, SSE = 0.103901.
TRAINBP: 1548/5000 epochs, SSE = 0.102331.
TRAINBP: 1549/5000 epochs, SSE = 0.103851.
TRAINBP: 1550/5000 epochs, SSE = 0.102282.
TRAINBP: 1551/5000 epochs, SSE = 0.103802.
TRAINBP: 1552/5000 epochs, SSE = 0.102234.
TRAINBP: 1553/5000 epochs, SSE = 0.103752.
TRAINBP: 1554/5000 epochs, SSE = 0.102186.
TRAINBP: 1555/5000 epochs, SSE = 0.103703.
TRAINBP: 1556/5000 epochs, SSE = 0.102138.
TRAINBP: 1557/5000 epochs, SSE = 0.103654.
TRAINBP: 1558/5000 epochs, SSE = 0.10209.
TRAINBP: 1559/5000 epochs, SSE = 0.103604.
TRAINBP: 1560/5000 epochs, SSE = 0.102042.
TRAINBP: 1561/5000 epochs, SSE = 0.103555.
TRAINBP: 1562/5000 epochs, SSE = 0.101994.
TRAINBP: 1563/5000 epochs, SSE = 0.103505.
TRAINBP: 1564/5000 epochs, SSE = 0.101946.
TRAINBP: 1565/5000 epochs, SSE = 0.103456.
TRAINBP: 1566/5000 epochs, SSE = 0.101899.
TRAINBP: 1567/5000 epochs, SSE = 0.103407.
TRAINBP: 1568/5000 epochs, SSE = 0.101851.
TRAINBP: 1569/5000 epochs, SSE = 0.103358.
TRAINBP: 1570/5000 epochs, SSE = 0.101803.
TRAINBP: 1571/5000 epochs, SSE = 0.103308.
TRAINBP: 1572/5000 epochs, SSE = 0.101755.
TRAINBP: 1573/5000 epochs, SSE = 0.103259.
TRAINBP: 1574/5000 epochs, SSE = 0.101707.
TRAINBP: 1575/5000 epochs, SSE = 0.10321.
TRAINBP: 1576/5000 epochs, SSE = 0.10166.
TRAINBP: 1577/5000 epochs, SSE = 0.103161.
TRAINBP: 1578/5000 epochs, SSE = 0.101612.
TRAINBP: 1579/5000 epochs, SSE = 0.103112.
TRAINBP: 1580/5000 epochs, SSE = 0.101564.
TRAINBP: 1581/5000 epochs, SSE = 0.103063.
TRAINBP: 1582/5000 epochs, SSE = 0.101517.
TRAINBP: 1583/5000 epochs, SSE = 0.103014.
TRAINBP: 1584/5000 epochs, SSE = 0.101469.
TRAINBP: 1585/5000 epochs, SSE = 0.102965.
TRAINBP: 1586/5000 epochs, SSE = 0.101422.
TRAINBP: 1587/5000 epochs, SSE = 0.102916.
TRAINBP: 1588/5000 epochs, SSE = 0.101374.
TRAINBP: 1589/5000 epochs, SSE = 0.102867.
TRAINBP: 1590/5000 epochs, SSE = 0.101327.
TRAINBP: 1591/5000 epochs, SSE = 0.102818.
TRAINBP: 1592/5000 epochs, SSE = 0.101279.
TRAINBP: 1593/5000 epochs, SSE = 0.102769.
TRAINBP: 1594/5000 epochs, SSE = 0.101232.
TRAINBP: 1595/5000 epochs, SSE = 0.10272.
TRAINBP: 1596/5000 epochs, SSE = 0.101184.
TRAINBP: 1597/5000 epochs, SSE = 0.102672.
TRAINBP: 1598/5000 epochs, SSE = 0.101137.
TRAINBP: 1599/5000 epochs, SSE = 0.102623.
TRAINBP: 1600/5000 epochs, SSE = 0.10109.
TRAINBP: 1601/5000 epochs, SSE = 0.102574.
TRAINBP: 1602/5000 epochs, SSE = 0.101042.
TRAINBP: 1603/5000 epochs, SSE = 0.102525.
TRAINBP: 1604/5000 epochs, SSE = 0.100995.
TRAINBP: 1605/5000 epochs, SSE = 0.102477.
TRAINBP: 1606/5000 epochs, SSE = 0.100948.
TRAINBP: 1607/5000 epochs, SSE = 0.102428.
TRAINBP: 1608/5000 epochs, SSE = 0.100901.
TRAINBP: 1609/5000 epochs, SSE = 0.10238.
TRAINBP: 1610/5000 epochs, SSE = 0.100854.
TRAINBP: 1611/5000 epochs, SSE = 0.102331.
TRAINBP: 1612/5000 epochs, SSE = 0.100806.
TRAINBP: 1613/5000 epochs, SSE = 0.102282.
TRAINBP: 1614/5000 epochs, SSE = 0.100759.
TRAINBP: 1615/5000 epochs, SSE = 0.102234.
TRAINBP: 1616/5000 epochs, SSE = 0.100712.
TRAINBP: 1617/5000 epochs, SSE = 0.102185.
TRAINBP: 1618/5000 epochs, SSE = 0.100665.
TRAINBP: 1619/5000 epochs, SSE = 0.102137.
TRAINBP: 1620/5000 epochs, SSE = 0.100618.
TRAINBP: 1621/5000 epochs, SSE = 0.102088.
TRAINBP: 1622/5000 epochs, SSE = 0.100571.
TRAINBP: 1623/5000 epochs, SSE = 0.10204.
TRAINBP: 1624/5000 epochs, SSE = 0.100524.
TRAINBP: 1625/5000 epochs, SSE = 0.101992.
TRAINBP: 1626/5000 epochs, SSE = 0.100477.
TRAINBP: 1627/5000 epochs, SSE = 0.101943.
TRAINBP: 1628/5000 epochs, SSE = 0.10043.
TRAINBP: 1629/5000 epochs, SSE = 0.101895.
TRAINBP: 1630/5000 epochs, SSE = 0.100383.
TRAINBP: 1631/5000 epochs, SSE = 0.101847.
TRAINBP: 1632/5000 epochs, SSE = 0.100336.
TRAINBP: 1633/5000 epochs, SSE = 0.101798.
TRAINBP: 1634/5000 epochs, SSE = 0.100289.
TRAINBP: 1635/5000 epochs, SSE = 0.10175.
TRAINBP: 1636/5000 epochs, SSE = 0.100242.
TRAINBP: 1637/5000 epochs, SSE = 0.101702.
TRAINBP: 1638/5000 epochs, SSE = 0.100196.
TRAINBP: 1639/5000 epochs, SSE = 0.101654.
TRAINBP: 1640/5000 epochs, SSE = 0.100149.
TRAINBP: 1641/5000 epochs, SSE = 0.101605.
TRAINBP: 1642/5000 epochs, SSE = 0.100102.
TRAINBP: 1643/5000 epochs, SSE = 0.101557.
TRAINBP: 1644/5000 epochs, SSE = 0.100055.
TRAINBP: 1645/5000 epochs, SSE = 0.101509.
TRAINBP: 1646/5000 epochs, SSE = 0.100009.
TRAINBP: 1647/5000 epochs, SSE = 0.101461.
TRAINBP: 1648/5000 epochs, SSE = 0.0999619.
w1 =
3.5632 -25.6480
4.8546 -24.8266
4.3388 -25.1834
10.6214 -15.7280
-12.7800 1.4011
-13.0789 -5.4966
10.1846 15.0656
-11.7914 10.8894
13.1998 0.4957
5.0734 -24.5733
b1 =
19.0274
17.2437
18.1958
6.9371
9.3447
16.3239
-21.3089
0.4556
-8.9675
19.8165
w2 =
Columns 1 through 5
-0.2872 -0.1543 -0.5391 -0.1642 -0.1407
0.3976 0.6868 0.1081 0.2792 0.8937
Columns 6 through 10
-1.0281 0.6234 -0.2088 0.0830 -0.5899
0.8067 -0.6606 0.0404 0.3923 0.1784
b2 =
1.9060
0.0454
Analisis :
1. Transformasi linear yang digunakan pada data asli kurang sesuai. Taburan data setelah transformasi masih bisa lebih stabil lagi jika digunakan transformasi data yang lain seperti transformasi polinomial ln atau normal
.
2. Komposisi pembagian data yang kurang tepat yang menyebabkan masalah overtraining. Tingkat keakuratan hasil ramalan dengan metode ARIMA masih lebih baik dibandingkan dengan jaringan syaraf tiruan yang terlihat dari nilai MAPE ARIMA yang lebih kecil dari jaringan syaraf tiruan. Tetapi jaringan
Rabu, 29 April 2009
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