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pause %按任意键看输入数据
clc
%输入样本数据
p=[ 1.2424 1.2424 1.2424 1.2424 1.2424 1.2424 200 200 200 200 200 200 400 400 400 400 ...
400 400 600 600 600 600 600 600 800 800 800 800 800 800; 1 1 1 400 400 400 1 1 1 400 400 400 ...
1 1 1 400 400 400 1 1 1 400 400 400 1 1 1 400 400 400; 200 350 500 200 350 500 200 350 500 200 ...
350 500 200 350 500 200 350 500 200 350 500 200 350 500 200 350 500 200 350 500 ];
%输入目标数据
t=[ -0.0000850 -2.7112000 0.3282800 0.0027129 1.0997000 2.7984000 -0.6427100 2.0863000 -0.4607700 ...
3.4253000 4.8946000 2.4998000 -2.2702000 -0.8187100 -0.6684200 -0.0532630 0.9908900 1.2144000 ...
-0.7455400 -0.5715200 -0.4054000 1.9441000 0.7021700 0.6903000 -6.4449000 -2.2801000 ...
0.1483200 -3.7869000 0.2750100 0.0515480 ];
net=newff([1 600],[5 1],{'tansig' 'purelin'},'trainlm'); %网络构建和初始化
net.trainParam.epochs=200; %最大训练步数Epoch
net.trainParam.goal=0.001; %误差平方和指标MSE
y = sim(net,t) %网络模拟
net = train(net,t); %网络训练
plot(p,t,'o',p,y,'*') %绘制网络逼近效果图
pause %按任意键看网络模拟测试
这样改可以运行 |
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