MATLAB 神经网络 例程
% 读入训练数据和测试数据
X = [];
Y1= [];
Y2= [];
Y3= [];
Y4= [];
A=[];
B=[];
str = {'Test4',};
Data = textread([str{1},'.txt']);
% 读训练数据
for i=1:40
X = [X Data(i:end-41+i-10,1:1) Data(i:end-41+i-10,2:2) Data(i:end-41+i-10,3:3) Data(i:end-41+i-10,1:1)];
end
Y1= Data(41:end-10,1:1);
Y2= Data(41:end-10,2:2);
Y3= Data(41:end-10,3:3);
Y4= Data(41:end-10,4:4);
A=X(end:end,1:end);
%Input = Input';
%Output = Output';
X=X';
Y3=Y3';
Y2=Y2';
Y1=Y1';
Y4=Y4';
% 矩阵赚置
[X,minx,maxx,Y1,miny1,maxy1] = premnmx(X,Y1);
% 标准化数据
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
% 神经网络参数设置
Para.Goal = 1;
% 网络训练目标误差
Para.Epochs = 800;
% 网络训练代数
Para.LearnRate = 0.8;
% 网络学习速率
%====
Para.Show = 200;
% 网络训练显示间隔
Para.InRange = repmat([-1 1],size(X,1),1);
% 网络的输入变量区间
%Para.Neurons = [size(X,1)*2+1 1];
Para.Neurons = [30 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(X,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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Net1 = newff(Para.InRange,Para.Neurons,Para.TransferFcn,...
Para.TrainFcn,Para.LearnFcn,Para.PerformFcn);
% 建立网络
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Net1.trainParam.show = Para.Show;
% 训练显示间隔赋值
Net1.trainParam.goal = Para.Goal;
% 训练目标误差赋值
Net1.trainParam.lr = Para.LearnRate;
% 网络学习速率赋值
Net1.trainParam.epochs = Para.Epochs;
% 训练代数赋值
Net1.trainParam.lr = Para.LearnRate;
Net1.performFcn = Para.PerformFcn;
% 误差函数赋值
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 调试
OutY1 =sim(Net1,X);
% 仿真刚建立的网络
Sse1 =sse(Y1-OutY1);
% 刚建立的网络误差
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[Net1 TR] = train(Net1,X,Y1);
% 训练网络并返回
% 矩阵赚置
[Y2,miny2,maxy2] = premnmx(Y2);
% 标准化数据
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
% 神经网络参数设置
Para.Goal =1;
% 网络训练目标误差
Para.Epochs = 800;
% 网络训练代数
Para.LearnRate = 0.8;
% 网络学习速率
%====
Para.Show = 200;
% 网络训练显示间隔
Para.InRange = repmat([-1 1],size(X,1),1);
% 网络的输入变量区间
%Para.Neurons = [size(X,1)*2+1 1];
Para.Neurons =
[30 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(X,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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Net2 = newff(Para.InRange,Para.Neurons,Para.TransferFcn,...
Para.TrainFcn,Para.LearnFcn,Para.PerformFcn);
% 建立网络
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Net2.trainParam.show = Para.Show;
% 训练显示间隔赋值
Net2.trainParam.goal = Para.Goal;
% 训练目标误差赋值
Net2.trainParam.lr = Para.LearnRate;
% 网络学习速率赋值
Net2.trainParam.epochs = Para.Epochs;
% 训练代数赋值
Net2.trainParam.lr = Para.LearnRate;
Net2.performFcn = Para.PerformFcn;
% 误差函数赋值
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 调试
OutY2 =sim(Net2,X);
% 仿真刚建立的网络
Sse2 =sse(Y2-OutY2);
% 刚建立的网络误差
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[Net2 TR] = train(Net2,X,Y2);
% 训练网络并返回
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 矩阵赚置
[Y3,miny3,maxy3] = premnmx(Y3);
% 标准化数据
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
% 神经网络参数设置
Para.Goal = 1;
% 网络训练目标误差
Para.Epochs = 800;
% 网络训练代数
Para.LearnRate = 0.8;
% 网络学习速率
%====
Para.Show = 200;
% 网络训练显示间隔
Para.InRange = repmat([-1 1],size(X,1),1);
% 网络的输入变量区间
%Para.Neurons = [size(X,1)*2+1 1];
Para.Neurons = [30 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(X,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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Net3 = newff(Para.InRange,Para.Neurons,Para.TransferFcn,...
Para.TrainFcn,Para.LearnFcn,Para.PerformFcn);
% 建立网络
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Net3.t
rainParam.show = Para.Show;
% 训练显示间隔赋值
Net3.trainParam.goal = Para.Goal;
% 训练目标误差赋值
Net3.trainParam.lr = Para.LearnRate;
% 网络学习速率赋值
Net3.trainParam.epochs = Para.Epochs;
% 训练代数赋值
Net3.trainParam.lr = Para.LearnRate;
Net3.performFcn = Para.PerformFcn;
% 误差函数赋值
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 调试
OutY3 =sim(Net3,X);
% 仿真刚建立的网络
Sse3 =sse(Y3-OutY3);
% 刚建立的网络误差
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[Net3 TR] = train(Net3,X,Y3);
% 训练网络并返回
% 矩阵赚置
[Y4,miny4,maxy4] = premnmx(Y4);
% 标准化数据
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
% 神经网络参数设置
Para.Goal =1;
% 网络训练目标误差
Para.Epochs = 800;
% 网络训练代数
Para.LearnRate = 0.8;
% 网络学习速率
%====
Para.Show = 200;
% 网络训练显示间隔
Para.InRange = repmat([-1 1],size(X,1),1);
% 网络的输入变量区间
%Para.Neurons = [size(X,1)*2+1 1];
Para.Neurons = [30 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(X,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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Net4 = newff(Para.InRange,Para.Neurons,Para.TransferFcn,...
Para.TrainFcn,Para.LearnFcn,Para.PerformFcn);
% 建立网络
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Net4.trainParam.show = Para.Show;
% 训练显示间隔赋值
Net4.trainParam.goal = Para.Goal;
% 训练目标误差赋值
Net4.trainParam.lr = Para.LearnRate;
% 网络学习速率赋值
Net4.trainParam.epochs = Para.Epochs;
% 训练代数赋值
Net4.trainParam.lr = Para.LearnRate;
Net4.performFcn = Para.PerformFcn;
% 误差函数赋值
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 调试
OutY4 =sim(Net4,X);
% 仿真刚建立的网络
Sse4 =sse(Y4-OutY4);
% 刚建立的网络误差
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[Net4 TR] = train(Net4,X,Y4);
% 训练网络并返回
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
T1=sim(Net1,X);
T2=sim(Net2,X);
T3=sim(Net3,X);
T4=sim(Net4,X);
[A1]=tramnmx(A',minx,maxx);
Out1 =sim(Net1,A1);
Out2 =sim(Net2,A1);
Out3 =sim(Net3,A1);
Out4 =sim(Net4,A1);
temp1=postmnmx(Out1,miny1,maxy1);
temp2=postmnmx(Out2,miny2,maxy2);
temp3=postmnmx(Out3,miny3,maxy3);
temp4=postmnmx(Out4,miny4,maxy4);
B=postmnmx(T4,miny4,ma
xy4);
for i=2:10
B=[B temp4];
A=[A(end:end,5:end) temp1 temp2 temp3 temp4];
[A1]=tramnmx(A',minx,maxx);
Out1 =sim(Net1,A1);
Out2 =sim(Net2,A1);
Out3 =sim(Net3,A1);
Out4 =sim(Net4,A1);
temp1=postmnmx(Out1,miny1,maxy1);
temp2=postmnmx(Out2,miny2,maxy2);
temp3=postmnmx(Out3,miny3,maxy3);
temp4=postmnmx(Out4,miny4,maxy4);
end
B=[B temp4];
% 对学习训练后的网络仿真
input=[41:1:200];
Data=Data(41:200,4:4);
plot(1:length(input),Data','r:',1:length(input),B,'k');