如何用MATLAB的神经网络工具箱实现三层BP网络

如何用MA TLAB的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