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close all

clear all

clc

a=x;

b=y;

inputs = a';

targets = b';

% 创建一个模式识别网络(两层BP网络)，同时给出中间层神经元的个数，这里使用20

net=newff(inputs,targets,[25 25]);

net.trainParam.epochs=100;

net.trainParam.lr=0.01;

net.trainParam.goal=0.0001;

net.trainParam.max_fail=20;

% 对数据进行预处理，这里使用了归一化函数(一般不用修改)

% For a list of all processing functionstype: help nnprocess

net.inputs{1}.processFcns ={'removeconstantrows','mapminmax'};

% 把训练数据分成三部分，训练网络、验证网络、测试网络

% For a list of all data division functionstype: help nndivide

net.divideFcn = 'dividerand'; % Divide data randomly

net.divideMode = 'sample'; % Divide up every sample

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

% 训练函数

% For a list of all training functionstype: help nntrain

net.trainFcn = 'trainlm'; % Levenberg-Marquardt

% 使用均方误差来评估网络

% For a list of all performance functionstype: help nnperformance

net.performFcn = 'mse'; % Mean squared error

% 画图函数

% For a list of all plot functions type:help nnplot

net.plotFcns ={'plotperform','plottrainstate','ploterrhist', ...

'plotregression', 'plotfit'};

net.divideFcn='';

% 开始训练网络(包含了训练和验证的过程)

[net,tr] = train(net,inputs,targets);

an=sim(net,inputs);

% 测试网络

outputs = net(inputs);

errors = gsubtract(targets,outputs);

performance = perform(net,targets,outputs);

% 获得训练、验证和测试的结果

trainTargets = targets .* tr.trainMask{1};

valTargets = targets .* tr.valMask{1};

testTargets = targets .* tr.testMask{1};

trainPerformance =perform(net,trainTargets,outputs);

valPerformance =perform(net,valTargets,outputs);

testPerformance =perform(net,testTargets,outputs);

% 可以查看网络的各个参数

view(net)

% 根据画图的结果，决定是否满意

% Uncomment these lines to enable variousplots.

figure, plotperform(tr)

figure, plottrainstate(tr)

figure, plotconfusion(targets,outputs)

figure, ploterrhist(errors)

figure, plot(errors,'-*')

figure, plot(an,':og')

hold on

plot(testTargets,'-*');

legend('预测输出','期望输出')

title('BP网络预测输出','fontsize',12)

ylabel('函数输出','fontsize',12)

xlabel('样本','fontsize',12)

程序总是训练六次就结束，添加net,divideFcn='';也没有作用