Pada postingan kali ini, ane mau bagi-bagi tugas kuliah ane Teknik Informatika tentang jaringan syaraf tiruan untuk pengenalan pola huruf latin dari A sampai Z menggunakan program matlab 7.0
TUGAS JARINGAN SYARAF TIRUAN
Muhammad Hasan (07650071)
Akan dibuat jaringan syaraf tiruan FeedForward untuk mengenali huruf A sampai Z dengan matriks 5x9 menggunakan matlab dengan lima buah hiden layer dan lima buah output. Bagian pertama adalah pengenalan untuk huruf normal, bagian kedua pengenalan untuk huruf normal ditambah huruf yang terdapat corrupt, dan bagian ketiga adalah pengenalan untuk huruf normal ditambah huruf yang terdapat corrupt ditambah huruf yang terdapat noise.
1. Pengenalan untuk huruf normal
Jumlah huruf 26 dan target 26, huruf-huruf tersebut telah ditranspose.
Matriks untuk huruf-huruf normal A-Z berjumlah 26
>>matrik_huruf
matrik_huruf=
0 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1
0 1 1 1 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1
1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1
0 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 1
0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0
1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0
0 1 1 0 0 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1
1 1 0 1 0 0 0 1 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0
0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0
0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 1 1
0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0
1 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0 0 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0
1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1 0 0
1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0
1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 0
1 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1
0 1 1 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0 1 0 1 0 0 0 0 1
0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 1 1 1 0 0 1 1
0 1 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 1
1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 0 0 1 1 0 1
>>
Target untuk huruf-huruf normal berjumlah 26
>> target_huruf
target_huruf =
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1
0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0
0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
>>
%membuat mlp feedforward
>> net=newff(minmax(matrik_normal),[5,5],{'logsig','logsig'})
** Warning in INIT
** Network "input{1}.range" has a row with equal min and max values.
** Constant inputs do not provide useful information.
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: 'trains'
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)
>>
%melakukan training
>> net=train(net,matrik_normal,target_normal);
TRAINLM, Epoch 0/100, MSE 0.375419/0, Gradient 4.4846/1e-010
TRAINLM, Epoch 25/100, MSE 2.7436e-014/0, Gradient 7.51257e-011/1e-010
TRAINLM, Minimum gradient reached, performance goal was not met.
>>
%melakukan simulasi
hasil=sim(net,matrik_huruf)
%mencoba untuk salah satu huruf, misal huruf A normal
>> sim(net,[0;0;1;0;0;0;1;0;1;0;1;0;0;0;1;1;0;0;0;1;1;0;0;0;1;1;1;1;1;1;1;0;0;0;1;1;0;0;0;1;1;0;0;0;1;])
ans =
0.0000
0.0000
0.0000
0.0000
1.0000
%Jaringan telah sukses mengenali pola sesuai target
2. Pengenalan huruf normal ditambah huruf yang terdapat corrupt
Matriks untuk huruf-huruf normal A-Z dan huruf-huruf yang terdapat corrupt berjumlah 52
Matrik huruf normal ditambah huruf corrupt:
>> matrik_huruf
matrik_huruf =
Columns 1 through 22
0 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 0 1 0 1 1 1
0 1 1 1 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 0
1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1 1 1 1 1 0 0
0 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0
0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 0 0 0 1 1 1
0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1
1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0
1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0
0 1 1 0 0 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1 0 0 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0
1 1 0 1 0 0 0 1 0 1 0 0 1 1 1 1 0 1 0 0 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1
0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0
0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 1 1 1 0 0
0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0
1 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0 0 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1
1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1
1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0
1 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0
0 1 1 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0 1 0 1 0
0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 1 1 1
0 1 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0
1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 0 0
Columns 23 through 44
1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 1
0 0 0 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 1 1 1 1
0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0
0 0 0 1 0 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 1
1 1 1 1 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0
1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0
1 1 1 1 0 1 1 0 0 0 1 1 0 1 1 0 1 1 1 1 0 1
1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0
1 1 1 1 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1
1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0
0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 0 1 1 1 1 0 1
1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1
0 1 1 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1
0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1
1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0
1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0
0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1
1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0
1 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1
1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1
1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 0 1 1 0 1 0 1
0 0 0 1 0 1 1 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0
0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0
0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0
1 1 0 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1
Columns 45 through 52
0 1 1 1 1 1 1 1
1 1 0 0 0 0 0 1
1 1 0 0 0 0 0 0
1 1 0 0 0 0 0 1
0 1 1 1 1 1 1 1
1 0 1 1 1 1 1 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
1 0 1 1 1 1 1 1
1 0 1 1 1 1 1 0
0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 1 1 1 1 1 1
1 0 1 1 1 0 0 0
0 0 0 0 0 1 1 0
0 1 0 0 0 0 0 0
0 0 0 0 0 1 1 1
0 0 1 1 1 0 0 0
0 0 1 1 1 0 0 0
1 0 0 0 0 0 0 0
0 1 0 0 0 0 0 1
1 0 0 0 0 0 0 0
0 0 1 1 1 0 0 0
0 0 1 1 1 0 0 0
0 0 0 0 0 1 0 1
0 1 0 0 0 0 1 0
0 0 0 0 0 1 0 0
1 0 1 1 1 0 0 0
0 0 1 1 1 1 0 1
0 0 0 0 0 0 0 0
0 1 0 0 0 0 1 0
0 0 0 0 0 0 0 0
1 0 1 1 1 1 0 0
1 0 1 0 1 1 0 1
0 0 0 1 1 0 0 0
0 1 0 0 0 0 1 0
0 0 0 1 1 0 0 0
1 0 1 0 1 1 0 0
0 0 0 0 1 1 0 1
1 0 1 0 0 0 0 1
1 1 0 0 0 0 1 1
1 0 1 0 0 0 0 1
0 0 0 0 1 1 0 1
%Target huruf huruf normal ditambah target huruf corrupt berjumlah juga 52
>> target_huruf
target_huruf =
Columns 1 through 22
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1
0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
Columns 23 through 44
1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0
1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0
1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
Columns 45 through 52
1 1 1 1 1 1 1 1
0 0 0 0 0 1 1 1
0 1 1 1 1 0 0 0
1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0
%melakukan training
>> net=train(net,matrik_huruf,target_huruf);
TRAINLM, Epoch 0/100, MSE 0.100116/0, Gradient 8.14553/1e-010
TRAINLM, Epoch 25/100, MSE 3.63124e-009/0, Gradient 2.45187e-005/1e-010
TRAINLM, Epoch 34/100, MSE 2.07308e-014/0, Gradient 3.11401e-011/1e-010
TRAINLM, Minimum gradient reached, performance goal was not met.
%Melakukan simulasi
%mencoba untuk salah satu huruf, misal huruf Z normal dan Z corrupt
>> Z=sim(net,[1;1;1;1;1;0;0;0;0;1;0;0;0;0;1;0;0;0;1;0;0;0;1;0;0;0;1;0;0;0;1;0;0;0;0;1;0;0;0;0;1;1;1;1;1;])
Z =
1.0000
1.0000
0.0000
1.0000
0.0000
>> Z_corrupt=sim(net,[1;1;0;1;1;0;0;0;0;1;0;0;0;0;1;0;0;0;1;0;0;0;1;0;0;0;1;0;0;0;1;0;0;0;0;1;0;0;0;0;1;1;1;1;1;])
Z_corrupt =
1.0000
1.0000
0.0000
1.0000
0.0000
%Jaringan telah sukses mengenali pola
3. Pengenalan Huruf Normal Ditambah Huruf yang Terdapat Corrupt Ditambah huruf yang Terdapat Noise
Matriks untuk huruf-huruf normal A-Z ditambah huruf-huruf yang terdapat corrupt ditambah huruf-huruf yang terdapat noise berjumlah 78
%Matrik huruf normal ditambah huruf corrupt:
>> matrik_huruf
matrik_huruf =
Columns 1 through 22
0 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 0 1 0 1 1 1
0 1 1 1 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 0
1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1 1 1 1 1 0 0
0 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0
0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 0 0 0 1 1 1
0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1
1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0
1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0
0 1 1 0 0 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1 0 0 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0
1 1 0 1 0 0 0 1 0 1 0 0 1 1 1 1 0 1 0 0 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1
0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0
0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 1 1 1 0 0
0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0
1 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0 0 1 1
1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1
1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0
1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1
1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0
1 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0
0 1 1 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0 1 0 1 0
0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 1 1 1
0 1 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0
1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 0 0
Columns 23 through 44
1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 1
0 0 0 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 1 1 1 1
0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0
0 0 0 1 0 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 1
1 1 1 1 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0
1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0
1 1 1 1 0 1 1 0 0 0 1 1 0 1 1 0 1 1 1 1 0 1
1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0
1 1 1 1 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1
1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0
0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 0 1 1 1 1 0 1
1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1
0 1 1 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1
0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1
1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0
1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0
0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1
1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0
1 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1
1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1
1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 0 1 1 0 1 0 1
0 0 0 1 0 1 1 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0
0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0
0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0
1 1 0 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1
Columns 45 through 66
0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 0 1 1 1 1
1 1 0 0 0 0 0 1 0 1 1 1 1 1 1 0 0 1 1 1 0 0
1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 0
1 1 0 0 0 0 0 1 0 1 1 0 1 1 1 1 0 1 0 0 0 0
0 1 1 1 1 1 1 1 0 0 0 0 1 1 0 1 0 1 1 0 1 1
1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0
1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 1 1
1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1 0 0 1 1
1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1
0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1
0 0 1 1 1 0 0 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1
0 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1
1 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0
0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 1 1 0 0 0 0 0
1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0
0 0 1 1 1 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 1 1
0 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1
0 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0
0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0
0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
1 0 1 1 1 0 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1
0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1
1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
1 0 1 0 1 1 0 0 1 1 1 0 0 0 1 1 0 1 1 0 1 1
0 0 0 0 1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1
1 0 1 0 0 0 0 1 0 1 1 1 1 0 1 0 0 1 0 1 0 0
1 1 0 0 0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 0
1 0 1 0 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 0
0 0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1
Columns 67 through 78
0 1 0 1 0 1 1 1 1 1 1 1
1 1 1 1 1 1 0 0 0 1 0 1
1 1 1 1 1 1 0 0 0 0 0 1
1 1 0 1 1 1 0 0 0 0 1 1
0 0 0 0 0 1 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0
0 0 1 0 1 0 0 0 0 0 0 0
1 1 0 1 1 0 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0
1 1 0 1 0 0 1 1 1 1 1 1
1 1 1 1 1 0 1 1 1 0 0 0
0 0 0 0 0 0 0 0 0 1 1 0
0 0 0 0 0 1 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 1 1 1
1 1 0 1 0 0 1 1 1 0 0 0
1 1 1 1 0 0 1 1 1 0 0 0
0 1 0 1 1 0 0 0 0 0 0 0
0 1 1 1 1 1 0 0 0 1 1 1
0 1 1 1 1 0 1 0 0 0 0 1
1 0 0 1 0 0 1 1 1 0 0 0
1 1 1 1 0 0 1 1 1 0 0 0
0 0 0 0 0 0 0 0 0 1 0 1
0 0 0 0 0 1 0 0 0 0 1 0
0 0 1 0 0 0 0 0 0 1 0 0
1 0 0 1 1 0 1 1 1 0 0 0
1 1 1 1 0 0 1 1 1 1 0 1
0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 1 0 0 1 0 1 0
0 0 1 0 0 0 0 0 0 0 0 0
1 0 0 1 1 0 1 1 1 1 0 0
1 1 1 1 1 0 1 0 1 1 0 1
0 0 0 0 0 0 0 1 1 0 0 0
1 0 0 0 0 1 0 1 1 0 1 0
0 0 1 0 0 0 0 1 1 0 0 0
1 0 0 1 1 0 1 0 1 1 0 0
0 1 0 1 0 0 0 0 1 1 0 1
1 1 1 0 1 0 1 0 0 0 0 1
1 0 1 0 1 1 1 1 0 0 1 1
1 0 0 0 1 1 1 0 0 0 0 1
0 0 1 1 0 0 0 0 1 1 0 1
%target huruf berjumlah juga 78
>> target_huruf
target_huruf =
Columns 1 through 22
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1
0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
Columns 23 through 44
1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0
1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0
1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
Columns 45 through 66
1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1
0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1
1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
Columns 67 through 78
0 1 1 1 1 1 1 1 1 1 1 1
1 0 0 0 0 0 0 0 0 1 1 1
1 0 0 0 0 1 1 1 1 0 0 0
1 0 0 1 1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0 1 0 1 0
%melakukan training
>> net=train(net,matrik_huruf,target_huruf);
TRAINLM, Epoch 0/100, MSE 0.0625688/0, Gradient 11.0361/1e-010
TRAINLM, Epoch 25/100, MSE 0.00775428/0, Gradient 0.821704/1e-010
TRAINLM, Epoch 47/100, MSE 0.00512821/0, Gradient 3.59352e-011/1e-010
TRAINLM, Minimum gradient reached, performance goal was not met.
%Melakukan simulasi
%mencoba untuk salah satu huruf, misal huruf B normal, B corrupt, B noise
>> B=sim(net,[1;1;1;1;0;1;0;0;0;1;1;0;0;0;1;1;0;0;0;1;1;1;1;1;0;1;0;0;0;1;1;0;0;0;1;1;0;0;0;1;1;1;1;1;0;])
B =
0.0000
0.0000
0.0000
1.0000
0.0000
>> Bcorrupt=sim(net,[0;1;1;1;0;1;0;0;0;1;1;0;0;0;1;1;0;0;0;1;1;1;1;1;0;1;0;0;0;1;1;0;0;0;1;1;0;0;0;1;1;1;1;1;0;])
Bcorrupt =
0.0000
0.0000
0.0000
1.0000
0.0000
>> Bnoise=sim(net,[1;1;1;1;0;1;0;0;0;1;1;0;0;0;1;1;0;0;0;1;1;1;1;1;1;1;0;0;0;1;1;0;0;0;1;1;0;0;0;1;1;1;1;1;0;])
Bnoise =
0.0000
0.0000
0.0000
1.0000
0.0000
%jaringan telah berhasil mengenali pola
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