如何用MATLAB的神经网络工具箱实现三层BP网络 如何在matlab中建立多层bp神经网络
\u5982\u4f55\u7528MATLAB\u7684\u795e\u7ecf\u7f51\u7edc\u5de5\u5177\u7bb1\u5b9e\u73b0\u4e09\u5c42BP\u7f51\u7edc\u8fd9\u662f\u4e00\u4e2a\u6765\u81eannetinfo\u7684\u4f8b\u5b50,\u5728matlab2012b\u8fd0\u884c\u540e\u7684\u786e\u53ef\u4ee5,\u56e0\u4e3a\u767e\u5ea6\u77e5\u9053\u7684\u6587\u672c\u5bbd\u5ea6\u4e0d\u591f,\u6ce8\u91ca\u6324\u5230\u7b2c\u4e8c\u884c\u4e86,\u6709\u4e9b\u4e71,\u697c\u4e3b\u6ce8\u610f\u533a\u5206\u54ea\u4e9b\u662f\u4ee3\u7801\u54ea\u4e9b\u662f\u6ce8\u91ca,
x1 =
[-3,-2.7,-2.4,-2.1,-1.8,-1.5,-1.2,-0.9,-0.6,-0.3,0,0.3,0.6,0.9,1.2,1.5,1.8]; %x1\uff1ax1 = -3:0.3:2;
x2 =
[-2,-1.8,-1.6,-1.4,-1.2,-1,-0.8,-0.6,-0.4,-0.2,-2.2204,0.2,0.4,0.6,0.8,1,1.2];%x2\uff1ax2 = -2:0.2:1.2;
y = [0.6589,0.2206,-0.1635,-0.4712,-0.6858,-0.7975,-0.8040,...
-0.7113,-0.5326,-0.2875
,0,0.3035,0.5966,0.8553,1.0600,1.1975,1.2618]; %y\uff1a
y = sin(x1)+0.2*x2.*x2;
inputData = [x1;x2]; %\u5c06x1,x2\u4f5c\u4e3a\u8f93\u5165\u6570\u636e
outputData = y; %\u5c06y\u4f5c\u4e3a\u8f93\u51fa\u6570\u636e
%\u4f7f\u7528\u7528\u8f93\u5165\u8f93\u51fa\u6570\u636e\uff08inputData\u3001outputData\uff09\u5efa\u7acb\u7f51\u7edc\uff0c
%\u9690\u8282\u70b9\u4e2a\u6570\u8bbe\u4e3a3.\u5176\u4e2d\u9690\u5c42\u3001\u8f93\u51fa\u5c42\u7684\u4f20\u9012\u51fd\u6570\u5206\u522b\u4e3atansig\u548cpurelin\uff0c\u4f7f\u7528trainlm\u65b9\u6cd5\u8bad\u7ec3\u3002
net = newff(inputData,outputData,3,{'tansig','purelin'},'trainlm');
%\u8bbe\u7f6e\u4e00\u4e9b\u5e38\u7528\u53c2\u6570
net.trainparam.goal = 0.0001;
%\u8bad\u7ec3\u76ee\u6807\uff1a\u5747\u65b9\u8bef\u5dee\u4f4e\u4e8e0.0001
net.trainparam.show = 400; %\u6bcf\u8bad\u7ec3400\u6b21\u5c55\u793a\u4e00\u6b21\u7ed3\u679c
net.trainparam.epochs = 15000;
%\u6700\u5927\u8bad\u7ec3\u6b21\u6570\uff1a15000.
[net,tr] = train(net,inputData,outputData);%\u8c03\u7528matlab\u795e\u7ecf\u7f51\u7edc\u5de5\u5177\u7bb1\u81ea\u5e26\u7684train\u51fd\u6570\u8bad\u7ec3\u7f51\u7edc
simout = sim(net,inputData);
%\u8c03\u7528matlab\u795e\u7ecf\u7f51\u7edc\u5de5\u5177\u7bb1\u81ea\u5e26\u7684sim\u51fd\u6570\u5f97\u5230\u7f51\u7edc\u7684\u9884\u6d4b\u503c
figure; %\u65b0\u5efa\u753b\u56fe\u7a97\u53e3\u7a97\u53e3
t=1:length(simout);
plot(t,y,t,simout,'r')%\u753b\u56fe\uff0c\u5bf9\u6bd4\u539f\u6765\u7684y\u548c\u7f51\u7edc\u9884\u6d4b\u7684y
\u5f53\u4f60\u7528newff\u7684\u65f6\u5019\uff0c\u91cc\u9762\u6709\u4e00\u4e2a\u53c2\u6570\u53ef\u4ee5\u63a7\u5236\u5c42\u6570\uff0c\u6bd4\u5982\u8bf4\uff1a
P = [0 1 2 3 4 5 6 7 8 9 10];
T = [0 1 2 3 4 3 2 1 2 3 4];
net = newff(P,T,5); %\u8fd9\u6837\u8868\u793a\u67091\u4e2a\u9690\u85cf\u5c42\uff0c\u91cc\u9762\u90a3\u4e2a5\u8868\u793a\u795e\u7ecf\u5143\u7684\u4e2a\u6570
net = newff(P,T,[5,10]); %\u8fd9\u6837\u8868\u793a\u67092\u4e2a\u9690\u85cf\u5c42
net = newff(P,T,[5 10 8]); %\u8fd9\u6837\u8868\u793a\u67093\u4e2a\u9690\u85cf\u5c42,\u4ee5\u6b64\u7c7b\u63a8\uff0c\u660e\u767d\u5427\uff1f
使用神经网络工具箱可以非常简便地实现网络建立和训练，实例代码如下：
%% BP算法
function Out=bpnet(p,t,p_test)
%p,t为样本需要提前组织好
global S1
net=newff(minmax(p),[S1,8],{'tansig','purelin'},'trainlm'); %trainlm训练函数最有效
%net=newff(P,T,31,{'tansig','purelin'},'trainlm');%新版用法
net.trainParam.epochs=1000;
net.trainParam.goal=0.00001;
net.trainParam.lr=0.01;
net.trainParam.showWindow = false; %阻止训练窗口的弹出
net.trainParam.showCommandLine = false; %阻止训练窗口的弹出
net=train(net,p,t);
Out=sim(net,p_test);
end
上面的代码不完整，完整的带训练样本数据的程序见附件。
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