dp22a_neu.m

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% Find a NN controller for DP22a problem
%  of Passino's Fuzzy control text, P.104
% dp22a_neu.m

% PenChen Chou, Aug. 5, 2001

% Input pattern
P=[  -1 -1;  -1 -0.8;  -1 -0.6;  -1 -0.4;  -1 -0.2;  -1 0;  -1 0.2;  -1 0.4;  -1 0.6;  -1 0.8;  -1 1;
   -0.8 -1;-0.8 -0.8;-0.8 -0.6;-0.8 -0.4;-0.8 -0.2;-0.8 0;-0.8 0.2;-0.8 0.4;-0.8 0.6;-0.8 0.8;-0.8 1;
   -0.6 -1;-0.6 -0.8;-0.6 -0.6;-0.6 -0.4;-0.6 -0.2;-0.6 0;-0.6 0.2;-0.6 0.4;-0.6 0.6;-0.6 0.8;-0.6 1;
   -0.4 -1;-0.4 -0.8;-0.4 -0.6;-0.4 -0.4;-0.4 -0.2;-0.4 0;-0.4 0.2;-0.4 0.4;-0.4 0.6;-0.4 0.8;-0.4 1;
   -0.2 -1;-0.2 -0.8;-0.2 -0.6;-0.2 -0.4;-0.2 -0.2;-0.2 0;-0.2 0.2;-0.2 0.4;-0.2 0.6;-0.2 0.8;-0.2 1;
      0 -1;   0 -0.8;   0 -0.6;   0 -0.4;   0 -0.2;   0 0;   0 0.2;   0 0.4;   0 0.6;   0 0.8;   0 1;
    0.2 -1; 0.2 -0.8; 0.2 -0.6; 0.2 -0.4; 0.2 -0.2; 0.2 0; 0.2 0.2; 0.2 0.4; 0.2 0.6; 0.2 0.8; 0.2 1;
    0.4 -1; 0.4 -0.8; 0.4 -0.6; 0.4 -0.4; 0.4 -0.2; 0.4 0; 0.4 0.2; 0.4 0.4; 0.4 0.6; 0.4 0.8; 0.4 1;
    0.6 -1; 0.6 -0.8; 0.6 -0.6; 0.6 -0.4; 0.6 -0.2; 0.6 0; 0.6 0.2; 0.6 0.4; 0.6 0.6; 0.6 0.8; 0.6 1;
    0.8 -1; 0.8 -0.8; 0.8 -0.6; 0.8 -0.4; 0.8 -0.2; 0.8 0; 0.8 0.2; 0.8 0.4; 0.8 0.6; 0.8 0.8; 0.8 1;
      1 -1;   1 -0.8;   1 -0.6;   1 -0.4;   1 -0.2;   1 0;   1 0.2;   1 0.4;   1 0.6;   1 0.8;   1 1];
P=P';
% Target
T1=[-1 -1 -1 -1 -1 -1 -0.8 -0.6 -0.4 -0.2 0];
T2=[-1 -1 -1 -1 -1 -0.8 -0.6 -0.4 -0.2 0 0.2];
T3=[-1 -1 -1 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4];
T4=[-1 -1 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6];
T5=[-1 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8];
T6=[-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1];
T7=[-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1];
T8=[-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1 1];
T9=[-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1 1 1];
T10=[-0.2 0 0.2 0.4 0.6 0.8 1 1 1 1 1];
T11=[0 0.2 0.4 0.6 0.8 1 1 1 1 1 1];
T=[T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11];
% New Feedforward NN
net = newff([-2 2;-2 2],[7 1],{'tansig' 'purelin'},'trainlm');
net.trainParam.epochs = 1500;
net.trainParam.goal = 1e-12;
% Training
net = train(net,P,T);
disp('Press <CR> to continue');pause
% Get the Neural output
Y = sim(net,P);
% Reprint the Target
% Display weights and bias
W10=net.IW{1,1}
B10=net.b{1}
W20=net.LW{2,1}
B10=net.b{2}
% Plot the target and neural outputs
sample=1:length(T);
figure(2);plot(sample,T,'o',sample,T,sample,Y)
title('Desired and Neural output plots')
xlabel('Sample points');
gensim(net,0.01)