小波程序.txt

c++做的小波分析程序

actualWithPredicted(:, 2) = predicted(1: pointsAhead); 
plot(actualWithPredicted); 
graph = ['Mean Absolute Error = ', num2str(mean(AbsError))]; 
title(graph); 
legend('Actual', 'Forecasted'); 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 

%File name :      nretrain.m 
%Description : This file loads the existing NNs and trains them again. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 

clc; 
%Prompt for the starting point for training 
disp('Program will now RETRAIN the Neural Networks ') 
disp('with the SAME intial data series again...'); 
disp(' '); 
disp('To capture the pattern of the signal, the model is ') 
disp('set to accept dataPoints x 2 sets of training examples; '); 
disp('1 set of demand + 1 sets of price. '); 
disp(' '); 
disp('The normalised demand data <point 2>, is to be taken as the ') 
disp('output value for the first iteration of training examples. '); 
disp(' '); 
msg = ['Data points to be used for reTraining the NNs is from 1 to ',... 
            num2str(dataPoints)]; 
disp(msg); 
disp(' '); 
disp('Press ENTER key to continue...'); 
pause; 

%Clear command screen 
clc; 

%Prompt for no. of training cycles 
%For current program, 1 cycle = user set no. of iterations (ie: dataPoints) 
cycle = input('Input number of cycles to retrain the NNs: '); 

numOfTimes = resolution + 1; 
%Loading existing NNs for training 
for x = 1: numOfTimes 

      %Re-initialising variables 
      clear targetDemand; 
      clear inputs; 
      clear output; 
      clc; 

      %Loading NN for the respective demand and temperature coefficient signals 
      filename = ['nn', num2str(x)]; 
      clear nn 
      load(filename); 

      %Getting the size of NN 
      numOfInputs = nn.nin; 
      numOfHiddens = nn.nhidden; 
      numOfOutput = 1; 

      %Setting length of reTraining examples and target outputs 
      reTrainLength = dataPoints; 
      targetLength = reTrainLength; 

      targetStartAt = 2; 

      %Set target outputs of the training examples 
      if(x == 1) 
            targetDemand = normDemand(targetStartAt: 1 + targetLength); 
      else 
            targetDemand = normDemandDetail(x - 1, targetStartAt: 1 + targetLength); 
      end 

      %Preparing training examples 
      %Setting training i/ps to be 2 with user set no. of iterations (dataPoints) 
      y = 0; 
      while y < reTrainLength 
            if(x == 1) 
                  inputs(1, y + 1) = normDemand(y + 1); 
                  inputs(2, y + 1) = normPrice(y + 1); 

            else 
                  inputs(1, y + 1) = normDemandDetail(x - 1, y + 1); 
                  inputs(2, y + 1) = normPriceDetail(x - 1, y + 1); 

            end 

            output(y + 1, :) = targetDemand(y + 1); 

            y = y + 1; 
      end 

      inputs = (inputs'); 

      %Setting no. of training cycles 
      [ni, np] = size(targetDemand); % <== [ni, np] tells the NN how long is 1 cycle; 
      size(targetDemand)                                %With reference to line 106 

      %NN options 
      options = zeros(1, 18); 
      options(1) = 1; %Provides display of error values 
      options(14) = cycle * ni * np; 

      %Training the neural network 
      %netopt(net, options, x, t, alg); 
      nn = netopt(nn, options, inputs, output, 'scg'); 

      %Save the neural network 
      filename = ['nn', num2str(x)]; 
      save(filename, 'nn'); 

      disp(' '); 
      msg = ['Neural network => ', filename, ' <= successfully RETRAINED and saved!! ']; 

      disp(msg); 

      if(x < 3) 
            disp(' '); 
            disp('Press ENTER key to continue training the next NN...'); 
      else 
            disp(' '); 
            disp('Model is now ready to forecast again!!'); 
            disp(' '); 
            disp('Press ENTER key to continue...'); 
      end 

      pause; 
end 
 小波神经网络程序 
gaoli 发表于 2007-8-2 16:37:00 
clc
clear
%step 1=========================
%定义输入样本；
t=0:0.01:1.5;
x=-sin(2*pi*t);
targ=[0 0 1 1 0 0 ];
eta=0.02;aerfa=0.935;
%初始化连接权wjh(输出层和隐层的连接权);whi(隐层和输出层的连接权);
%假设小波函数节点数为：H个；样本数为P;
%输出节点数为:J个；输入节点数为：I个；
H=15;P=2;
I=length(t);
J=length(targ);
%初始化小波参数
b=rand(H,1);
a=rand(H,1);
%初始化权系数；
whi=rand(I,H);
wjh=rand(H,J);
%阈值初始化;
b1=rand(H,1);
b2=rand(J,1);
p=0;
%保存的误差；
Err_NetOut=[];
flag=1;count=0;
while flag>0
flag=0;
count=count+1;

%step 2=================================
xhp1=0;
for h=1:H
for i=1:I
xhp1=xhp1+whi(i,h)*x(i);
end
ixhp(h)=xhp1+b1(h);
xhp1=0;
end
for h=1:H
oxhp(h)=fai((ixhp(h)-b(h))/a(h));
end
%step 3====================================
ixjp1=0;
for j=1:J
for h=1:H
ixjp1=ixjp1+wjh(h,j)*oxhp(h);
end
ixjp(j)=ixjp1+b2(j);
ixjp1=0;
end
for i=1:J
oxjp(i)=fnn(ixjp(i));
end
%step 6==保存每次误差=====
wuchayy=1/2*sumsqr(oxjp-targ);
%E_x=1/2*sumsqr(x);
Err_NetOut=[Err_NetOut wuchayy];%保存每次的误差;
%Err_rate=Err_NetOut/E_x;

%Err_rate

%oxjp

%求detaj ,detab2==================================
for j=1:J
detaj(j)=-(oxjp(j)-targ(j))*oxjp(j)*(1-oxjp(j));
end

for j=1:J
for h=1:H
detawjh(h,j)=eta*detaj(j)*oxhp(h);
end
end
detab2=eta*detaj;

%求detah, detawhi detab1 detab detaa;========================

sum=0;
for h=1:H
for j=1:J
sum=detaj(j)*wjh(h,j)*diffai((ixhp(h)-b(h))/a(h))/a(h)+sum;
end
detah(h)=sum;
sum=0;
end
for h=1:H
for i=1:I
detawhi(i,h)=eta*detah(h)*x(i);
end
end
detab1=eta*detah;
detab=-eta*detah;
for h=1:H
detaa(h)=-eta*detah(h)*((ixhp(h)-b(h))/a(h));
end

%引入动量因子aerfa，修正各个系数==========================================
wjh=wjh+(1+aerfa)*detawjh;
whi=whi+(1+aerfa)*detawhi;
a=a+(1+aerfa)*detaa';
b=b+(1+aerfa)*detab';
b1=b1+(1+aerfa)*detab1';
b2=b2+(1+aerfa)*detab2';

%======================================================
%引入修正算法！！

%判断所有的样本是否计算完==================================
p=p+1;
if p~=P
flag=flag+1;
else
if Err_NetOut(end)>0.05
flag=flag+1;

else
figure;
plot(Err_NetOut);
title('误差曲线');
disp('目标达到');
%disp(oxjp); 
end
end
if count>2000
figure;
plot(Err_NetOut);
title('误差曲线');
disp('目标未达到');
disp(oxjp);
break;
end
end 



程序代码 diffai.m
function y3=diffai(x);
    y3=-0.75*sin(1.75*x)*exp(-x.^2/2)-cos(1.75*x)*exp(-x.^2/2)*x; 


 程序代码 fai.m
function yl=fai(x)
    yl=cos(0.75.*x)+exp(-x.^2/2);
 
程序代码 fnn.m

function y2=fnn(x)
    y2=1/(1+exp(-x)); 
  用小波函数构建神经网络的源程序 
gaoli 发表于 2007-8-2 16:42:00 
该程序是用小波函数构建神经网络的源程序。用以分析心电信号、脑电信号等等。


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all
load data
M1=20;
epo=15;
A=4;
B=18;
B2=B/2+1
N=500;
M=(A+1)*(B+1);
for  a0=1:A+1;
for  b0=1:B+1;
i=(B+1)*(a0-1)+b0;
b_init(i)=((b0-B2)/10)/(2^(-A)); a_init(i)=1/(2^(-A));
c_init(i)=(20-A)/2;
end 
end 
w0=ones(1,M);
          for i=1:N             
              for j=1:M           
                  t=x(i);
                  t= a_init(j)*t-b_init(j);
                 %P0(i,j)= (cos(1.75*t)*exp(-t*t/2))/2^c_init(j); 
                 P0(i,j)= ((1-t*t)*exp(-t*t/2))/2^c_init(j); 
             end
          end
%calculation of output of network
           for i=1:N
                 u=0;
                 for j=1:M
                     u=u+w0(j)*P0(i,j);%w0?aè¨?μ
                 end
                 y0(i)= u;% y(p)= u=??W(j)*phi(p,j)= ??W(j)* |μj(t)
           end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
for k=1:M
    W(:,k)=P0(:,k);
end
for k=2:M
    u=0;
   
     for      i=1:k-1    
              aik(i)=(P0(:,k)'*W(:,i))/(W(:,i)'*W(:,i));
              u=u+aik(i) *W(:,i);
              
     end
     W(:,k)=P0(:,k)-u;
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for   i=1:M                         
     g(i)= (W(:,i)'*d')/( W(:,i)'* W(:,i)); 
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
u=0;
for    i=1:M
       u=u+g(i)*(W(:,i)'*W(:,i));
end

DD=u;

for    i=1:M
       Erro(i)=(g(i)^2)*(W(:,i)'*W(:,i))/DD;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
k=1;

while(k<=M1)
    u=1;
    for  i=2:M
         if   abs(Erro(u))<abs(Erro(i));
              u=i
         else u=u
         end
    end
    I(k)=u;
    Erro(u)=0
k=k+1;  
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
for k=1:M1
    u=I(k);
    a(k)=a_init(u);
    b(k)=b_init(u);
    c(k)=c_init(u);
    w1(k)=w0(u);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
epoch=1;

error=0.1;
err=0.0001;
lin=0.5;

while (error>=err & epoch<=epo)
          for i=1:N             
              for j=1:M1        
                  t=x(i);
                  t= a(j)*t-b(j);
                 %P1(i,j)= (cos(1.75*t)*exp(-t*t/2))/2^c(j); 
                 P1(i,j)= ((1-t*t)*exp(-t*t/2))/2^c(j); 
             end
          end
%calculation of output of network
           for i=1:N
                 u=0;
                 for j=1:M1
                     u=u+w1(j)*P1(i,j);
                 end
                 y1(i)= u;
           end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   u=0;
     for i=1:N
            u=u+(d(i)-y1(i))^2;
     end
    u=u/2;%u=1/2??(d-p)^2
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


if   u>error
lin=lin*0.8;
else
lin=lin*1.2;
end
error=u; %error=u=1/2??(d-p)^2
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
         for j=1:M1   
             u=0;
             for i=1:N
                 u=u+(d(i)-y1(i))*P1(i,j);
             end
             EW(j)=-u;
          end
   if    epoch==1
       SW=-EW;
       w1_=w1;
       
   else
   SW=-EW+((EW*EW')*SW_)/(EW_*EW_');
   end
   EW_=EW;
   SW_=SW;
    w1=w1_+SW*lin;
    w1_=w1;
  
    %number of epoch increase by 1
epoch=epoch+1;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
subplot(2,1,1);plot(x,d);
subplot(2,1,2);plot(x,y1);