p1 = sin(1:20);
p2 = sin(1:20)*2;
t1 = ones(1,20);
t2 = ones(1,20)*2;
%產(chǎn)生訓練樣本p和t
p = [p1 p2 p1 p2];
t = [t1 t2 t1 t2];
Pseq = con2seq(p);
Tseq = con2seq(t);
R = 1; % 輸入元素的數(shù)目為1
S2 = 1; % 輸出層的神經(jīng)元個數(shù)為1
S1 = 10; %中間層有10個神經(jīng)元
net=newelm([-2,2],[S1,S2],{'tansig','purelin'});
%設定網(wǎng)絡訓練次數(shù)
net.trainParam.epochs=300;
net=train(net,Pseq,Tseq);
y=sim(net,Pseq);
figure;
plot(t5,cat(2,y{:}),t5,cat(2,Tseq{:}),'b--');
%利用新的信號來測試網(wǎng)絡
p3 = sin(1:20)*1.6;
t3 = ones(1,20)*1.6;
p4 = sin(1:20)*1.2;
t4 = ones(1,20)*1.2;
%產(chǎn)生測試樣本pg和tg
pg = [p3 p4 p3 p4];
tg = [t3 t4 t3 t4];
pgseq = con2seq(pg);
a = sim(net,pgseq);
figure;
plot(t5,cat(2,a{:}),t5,tg,'b--');