某个例子.m

神经网络相关的代码

用神经网络工具箱实现三层BP网络
% 读入训练数据和测试数据 
Input = []; 
Output = []; 
str = {'Test','Check'}; 
Data = textread([str{1},'.txt']); 
% 读训练数据 
Input = Data(:,1:end-1); 
% 取数据表的前五列（主从成分） 
Output = Data(:,end); 
% 取数据表的最后一列（输出值） 
Data = textread([str{2},'.txt']); 
% 读测试数据 
CheckIn = Data(:,1:end-1); 
% 取数据表的前五列（主从成分） 
CheckOut = Data(:,end); 
% 取数据表的最后一列（输出值） 
Input = Input'; 
Output = Output'; 
CheckIn = CheckIn'; 
CheckOut = CheckOut'; 
% 矩阵赚置 
[Input,minp,maxp,Output,mint,maxt] = premnmx(Input,Output); 
% 标准化数据 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
% 神经网络参数设置 
%====可以修正处 
Para.Goal = 0.0001; 
% 网络训练目标误差 
Para.Epochs = 800; 
% 网络训练代数 
Para.LearnRate = 0.1; 
% 网络学习速率 
%==== 
Para.Show = 5; 
% 网络训练显示间隔 
Para.InRange = repmat([-1 1],size(Input,1),1); 
% 网络的输入变量区间 
Para.Neurons = [size(Input,1)*2+1 1]; 
% 网络后两层神经元配置 
Para.TransferFcn= {'logsig' 'purelin'}; 
% 各层的阈值函数 
Para.TrainFcn = 'trainlm'; 
% 网络训练函数赋值 
% traingd : 梯度下降后向传播法 
% traingda : 自适应学习速率的梯度下降法 
% traingdm : 带动量的梯度下降法 
% traingdx : 
% 带动量，自适应学习速率的梯度下降法 
Para.LearnFcn = 'learngdm'; 
% 网络学习函数 
Para.PerformFcn = 'sse'; 
% 网络的误差函数 
Para.InNum = size(Input,1); 
% 输入量维数 
Para.IWNum = Para.InNum*Para.Neurons(1); 
% 输入权重个数 
Para.LWNum = prod(Para.Neurons); 
% 层权重个数 
Para.BiasNum = sum(Para.Neurons); 
% 偏置个数 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
Net = newff(Para.InRange,Para.Neurons,Para.TransferFcn,... 
Para.TrainFcn,Para.LearnFcn,Para.PerformFcn); 
% 建立网络 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
Net.trainParam.show = Para.Show; 
% 训练显示间隔赋值 
Net.trainParam.goal = Para.Goal; 
% 训练目标误差赋值 
Net.trainParam.lr = Para.LearnRate; 
% 网络学习速率赋值 
Net.trainParam.epochs = Para.Epochs; 
% 训练代数赋值 
Net.trainParam.lr = Para.LearnRate; 

Net.performFcn = Para.PerformFcn; 
% 误差函数赋值 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
% 调试 
Out1 =sim(Net,Input); 
% 仿真刚建立的网络 
Sse1 =sse(Output-Out1); 
% 刚建立的网络误差 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
[Net TR] = train(Net,Input,Output); 
% 训练网络并返回 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
Out3 =sim(Net,Input); 
% 对学习训练后的网络仿真