untitled6.m

matlab 编写的小波分解程序,进行模式识别

close all
clear 
echo on 
clc 
%NEWFF---生成一个新的前向神经网络
%TRAIN---对BP神经网络进行训练
%SIM-----对BP神经网络进行仿真
pause    %敲任意键开始
clc 

%定义训练样本

%P为输入矢量 
P=[87.196,118.5924,124.2915,129.1929,139.3846,175.6648,237.1847,415.2695,2.20E+03,1.94E+04,3.84E+04];
%T为目标矢量
T=[0,0,0,0,0,0,0,0,0,0,0.1];
pause;
clc 
%创建一个新的前向神经网络
net=newff(minmax(P),[5,1],{'tansig','purelin'},'trainlm');
%当前输入层权值和总阈值
inputWeights=net.IW{1,1};
inputbias=net.b{1};
%当前网络层权值和阈值
layerWeights=net.LW{2,1};
layerbias=net.b{2};
pause 
clc 
%设置训练参数
net.trainParam.show=50;    %
net.trainParam.lr=0.05;   %设定修正权值的学习速率  0.01~0.07
net.trainParam.mc=0.9;    %
net.trainParam.epochs=10000;  %设定循环次数
net.trainParam.goal=1e-3;    %设定期望误差最小值
pause 
clc 
%调用TRAINGDM算法训练网络
[net,tr]=train(net,P,T);
pause 
clc 
%对BP网络进行仿真
A=sim(net,P);
%计算仿真误差

E=T-A
MSE=mse(E)
pause
clc
echo off