matlab 神经网络

P=[1,1,1;
1,2,3;
4,5,6;
7,8,9;
2,3,4;
3,5,7;
1,3,5;
2,4,6;
1,2,5;
1,4,7;
1,3,8;
1,4,9;
2,5,9
2,6,0;
4,6,9]’;
T=[3,6,15,24,9,15,9,12,8,12,12,14,17,8,19]; % T 为目标矢量

[PP,ps]=mapminmax(P,-1,1);
[TT,ps]=mapminmax(T,-1,1);
net=newff(minmax(PP),[2,1],{‘tansig’,‘purelin’},‘traingdm’)
inputWeights=net.IW{1,1}
%当前输入层权值和阈值
inputbias=net.b{1}
% 当前网络层权值和阈值
layerWeights=net.LW{2,1}
layerbias=net.b{2}
% 设置网络的训练参数
net.trainParam.show = 2;
net.trainParam.lr = 0.05;
net.trainParam.mc = 0.9;
net.trainParam.epochs =10000;
net.trainParam.goal = 1e-3;
% 调用 TRAINGDM 算法训练 BP 网络 (在构建net中有说明)
[net,tr]=train(net,PP,TT);
A = sim(net,PP) % 对 BP 网络进行仿真，
A=mapminmax(‘reverse’,A,ps) % 对A矩阵进行反归一化处理（） % 计算仿真误差
E = T - A
MSE=mse(E)

inputWeights=net.IW{1,1}
%当前输入层权值和阈值
inputbias=net.b{1}
% 当前网络层权值和阈值
layerWeights=net.LW{2,1}
layerbias=net.b{2}
echo off

inputWeights =

1.0978   -1.3011    0.4617
1.0148    1.0337   -1.0064

inputbias =

-1.7639
1.7639

layerWeights =

-0.4430 0.0938

layerbias =

0.9150

A =

Columns 1 through 10

-0.9860 -0.7212 0.1437 0.9805 -0.3974 0.1329 -0.4424 -0.1482 -0.5587 -0.1733

Columns 11 through 15

-0.1056 0.0850 0.2641 -0.5102 0.5802

A =

Columns 1 through 10

3.1469    5.9272   15.0089   23.7948    9.3270   14.8951    8.8545   11.9435    7.6333   11.6801

Columns 11 through 15

12.3909 14.3925 16.2735 8.1433 19.5919

E =

Columns 1 through 10

-0.1469 0.0728 -0.0089 0.2052 -0.3270 0.1049 0.1455 0.0565 0.3667 0.3199

Columns 11 through 15

-0.3909 -0.3925 0.7265 -0.1433 -0.5919

MSE =

0.1102

inputWeights =

0.4501   -0.0385    0.6441
0.6781    1.3654    0.2104

inputbias =

-0.5015
1.9179

layerWeights =

1.0497    0.3944

layerbias =

0.0575