急～小波神经网络
clear all%initiate of data
P=3 %numberof sample
m=1%number of input node
n=10%number of hidden node
N=1%number of ouptut node
%
%a(n) b(n) scale and shifting parameter matrix
%x(P,m) input matrix of P sample
%net(P,n) ouput of hidden node
%y(P,N) output of network
%d(P,N) ideal output of network
% phi(P,n) ouput of hidden node wavelet funciton
%W(N,n)weight value between ouput and hidden
%WW(n,m) weight value betweenhidden and input node
x=
d=
W=rand(N,n)
WW=rand(n,m)
a=ones(1,n)
for j=1:n
b(j)=j*P/n;
end
%%%%%%%%%%%%%%%%%%
%EW(N,n) gradient of W
%EWW(n,m) gradient of WW
%Ea(n) gradient of a
%Eb(n) gradient of b
%%%%%%%%%%%%%%]
epoch=1;
epo=100;
error=0.05;
err=0.01;
delta =1;
lin=0.5;
while (error>=err & epoch<=epo)
u=0;%u is the middle variant
%caculation of net input
for p=1:P
for j=1:n
u=0;
for k=1:m
u=u+WW(j,k)*x(p,k);
end
net(p,j)=u;
end
end
%calculation of morlet 0r mexican wavelet output
for p=1:P
for j=1:n
u=net(p,j);
u=(u-b(j))/a(j);
phi(p,j)=cos(1.75*u)*exp(-u*u/2); %morlet wavelet
%phi(p,j)=(1-u^2)*exp(-u*u/2); %mexican hat wavelet
end
end
%calculation of output of network
for p=1:P
for i=1:N
u=0;
for j=1:n
u=u+W(i,j)*phi(p,j);
end
y(p,i)=delta*abs(u);
end
end
%calculation of error of output
u=0;
for p=1:P
for i=1:N
%u=u+abs(d(p,i)*log(y(p,i))+(1-d(p,i)*log(1-y(p,i))));
u=u+(d(p,i)-y(p,i))^2;
end
end
%u=u/2
error=u;
%calculate of gradient of network
for i=1:N
for j=1:n
u=0;
for p=1:P
u=u+(d(p,i)-y(p,i))*phi(p,j);
end
EW(i,j)=u;
%EW(i,j)=-u;%the resule would be wrong
end
end
for j=1:n
for k=1:m
u=0
for p=1:P
for i=1:N
u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)*x(p,k)/a(j) ;
end
end
EWW(j,k)=u;
%EWW(j,k)=u the result would be wrong
end
end
for j=1:n
u=0
for p=1:P
for i=1:N
u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)/a(j) ;
end
end
Eb(j)=u;
end
for j=1:n
u=0
for p=1:P
for i=1:N
u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)*((net(p,j)-b(j))/b(j))/a(j) ;
end
end
Ea(j)=u;
end
%adjust of weight value
WW=WW-lin*EWW;
W=W-lin*EW;
a=a-lin*Ea;
b=b-lin*Eb;
%number of epoch increase by 1
epoch=epoch+1;
end
为啥用以上程序进行小波模式识别处理，得到的U全等于0...（运行时有加数据样本的）
各位大侠帮帮忙～1月6号就要验收啦。。。十万火急先谢谢了
邮箱：chenxinben2005@163.com
[ 本帖最后由 chincat 于 2009-1-3 21:50 编辑 ]
回复 楼主 chincat 的帖子
“用以前程序进行小波模式识别处理”什么意思？回复 沙发 ch_j1985 的帖子
用以上程序 ，，，打太快打错了～急啊！！
我想求一下小波神经网络识别和提取电压畸变的程序。回复 楼主 chincat 的帖子
小波我不懂! 粗略看了下楼主的程序! 太乱了, 改成个人习惯其中红色部分修改下, 就不会反复输出u=0!
clear all; P=3; m=1; n=10; N=1; x=; d=; W=rand(N,n); WW=rand(n,m); a=ones(1,n);
for j=1:n, b(j)=j*P/n; end; epoch=1; epo=100; error=0.05; err=0.01; delta =1; lin=0.5;
while (error>=err & epoch<=epo), u=0; %u is the middle variant
% caculation of net input
for p=1:P, for j=1:n, u=0; for k=1:m, u=u+WW(j,k)*x(p,k); end; net(p,j)=u; end; end
% calculation of morlet 0r mexican wavelet output
for p=1:P, for j=1:n, u=net(p,j); u=(u-b(j))/a(j); phi(p,j)=cos(1.75*u)*exp(-u*u/2);end; end
% calculation of output of network
for p=1:P, for i=1:N, u=0; for j=1:n, u=u+W(i,j)*phi(p,j); end; y(p,i)=delta*abs(u); end; end
% calculation of error of output
u=0; for p=1:P, for i=1:N, u=u+(d(p,i)-y(p,i))^2; end; end; error=u;
% calculate of gradient of network
for i=1:N, for j=1:n, u=0; for p=1:P,u=u+(d(p,i)-y(p,i))*phi(p,j); end; EW(i,j)=u; end; end
for j=1:n, for k=1:m, u=0; for p=1:P, for i=1:N, u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)*x(p,k)/a(j) ; end; end; EWW(j,k)=u; end; end
for j=1:n, u=0; for p=1:P, for i=1:N, u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)/a(j) ; end; end; Eb(j)=u; end
for j=1:n, u=0; for p=1:P, for i=1:N, u=u+(d(p,i)-y(p,i))*W(i,j)*phi(p,j)*((net(p,j)-b(j))/b(j))/a(j) ; end; end; Ea(j)=u; end
WW=WW-lin*EWW; W=W-lin*EW; a=a-lin*Ea; b=b-lin*Eb; epoch=epoch+1; % adjust of weight value
end
[ 本帖最后由 ChaChing 于 2009-1-4 23:36 编辑 ]
回复 5楼 ChaChing 的帖子
谢谢大侠帮忙啦
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