wnn_test.m

遗传算法优化小波神经网络的源程序: 1.构造的非线性函数: 位于nninit_test.m 2.直接用WNN逼近非线性:Wnn_test.m, (内部调用小波函数) 3.遗传算法优化后逼近 :

clc;
clear;
close all;
%%%%%%%%%%%  sample
[P,T,R,S1,S2,S,Q]=nninit_test;

p=[1 0 0 0 0;
   1 0 0 0 1;
   1 1 0 0 1;
   1 0 1 0 0;
   1 0 0 1 0;
   1 0 0 0 1;
   0 1 0 0 0;
   0 0 1 0 0;
   0 0 0 1 0;
   0 0 0 0 1;
   0 1 1 1 1;
   0 0 1 1 1;
   0 0 0 1 1;
   0 0 0 0 1];
   t=[0 0 0 0;
     1 0 0 0;
     0 1 0 0;
     0 0 1 0;
     0 0 0 1;
     1 0 0 1;
     1 0 1 0;
     1 1 0 0; 
     1 1 0 1;
     0 1 1 0;
     0 1 1 1;
     0 1 0 1;
     1 0 1 1;
     1 1 1 1];
%产生随机矩阵以便随机调用训练数据

tic;
%初始化网络 
IN=R;       
HN=S1;      
ON=S2;
N=300;
derros=zeros(1,(N+2));
studyspace=zeros(1,(N+2));
studyspace(1,2)=0.002;
amlf=1.001;
betat=0.999;
kkk=1.04;
mc=0.01;
W11=randn(S1,R);
W22=randn(S2,S1);
B11=randn(S1,1);
B22=randn(S1,1);


v(:,:,2)=W11;
w=zeros(1,HN,N+2);
%W2(:,:,1)=rand(1,S1);
w(:,:,2)=W22;
a=zeros(1,HN,N+2);
a(:,:,2)=B11';
b=zeros(1,HN,N+2);
b(:,:,2)=B22';
%cshab;
mse=zeros(1,N+2);
ee=zeros(1,N+2);

%数据进行训练
for n=3:(N+2)

wdel=zeros(1,HN);
vdel=zeros(HN,IN); 
adel=zeros(1,HN);
bdel=zeros(1,HN);
wincrease=zeros(1,HN);
vincrease=zeros(HN,IN);
aincrease=zeros(1,HN);
bincrease=zeros(1,HN);
m=1;
while m<Q+1

in=P(:,m);

r=zeros(1,HN); 
z=zeros(1,HN);
dz=zeros(1,HN);
s=0;
for j=1:HN
for i=1:IN
r(1,j)=r(1,j)+v(j,i,n-1)*in(i,1);
end
b1=b(1,j,n-1);
a1=a(1,j,n-1);
z(1,j)=hfun(r(1,j),b1,a1);
dz(1,j)=dhfun(r(1,j),b1,a1);
s=s+w(1,j,n-1)*z(1,j); 
end
y(m)=s;
EEE=sqrt(abs(y(m)-T(m)));
e=(T(m)-y(m));
for j=1:HN
wdel(1,j)=wdel(1,j)+e*z(1,j) *1.5;            %%%%  修改梯度学习效率系数取得好的效果
for i=1:IN
vdel(j,i)=vdel(j,i)+  1.5* e*w(1,j,n-1)*dz(1,j)*in(i,1)/a(1,j,n-1);
end
adel(1,j)=adel(1,j)+e *w(1,j,n-1)*dz(1,j)*((r(1,j)-b(1,j,n-1))/a(1,j,n-1))/a(1,j,n-1); 
bdel(1,j)=bdel(1,j)+e *w(1,j,n-1)*dz(1,j)/a(1,j,n-1);
end

derros(1,n)=derros(1,n)+abs( T(m)*log(y(m))+(1-T(m) )*log(1-y(m)) ) ;


mse(1,n)=mse(1,n)+(y(m)-T(m)).^2;
MSE(1,n)=sqrt(mse(1,n));
m=m+1;
end
%plot(n,EEE); 
ee(1,n)=EEE;
for j=1:HN
wdel(1,j)=-wdel(1,j);
for i=1:IN
vdel(j,i)=-vdel(j,i);
end
end
derros(1,n)=-derros(1,n);

% 学习速率的调整
if derros(n)<derros(n-1)
studyspace(1,n)=amlf*studyspace(1,n-1); 
end
if derros(n)>=kkk*derros(n-1)
studyspace(1,n)=betat*studyspace(1,n-1);
end
for j=1:HN
wincrease(1,j)=-studyspace(1,n)*wdel(1,j)+mc*(w(1,j,n-1)-w(1,j,n-2));
w(1,j,n)=w(1,j,n-1)+wincrease(1,j);
for i=1:IN
vincrease(j,i)=-studyspace(1,n)*vdel(j,i)+mc*(v(j,i,n-1)-v(j,i,n-2));
v(j,i,n)=v(j,i,n-1)+vincrease(j,i);
end
aincrease(1,j)=-studyspace(1,n)*adel(1,j)+mc*(a(1,j,n-1)-a(1,j,n-2));
a(1,j,n)=a(1,j,n-1)+aincrease(1,j); 
bincrease(1,j)=-studyspace(1,n)*bdel(1,j)+mc*(b(1,j,n-1)-b(1,j,n-2));
b(1,j,n)=b(1,j,n-1)+bincrease(1,j);
end
end
%网络误差曲线
toc
plot(MSE(3:N+2)/Q); title('直接用 WNN 逼迫的误差')

figure(3)
plot(T,'r');    %title('目标信号')
hold on 
plot(y,'g*') ;  %title('WNN输出信号')