fuzzy.m.txt

一个模糊神经网络matlab程序

clear
clc
tic,
[x,y]=data;
%x=[1 1 1;
%   1 2 3];
%y=[2 3 4];  %%%%%--数据显示，输入为－两输入，输出为－单输出。--------样本为p2组
[p1,p2]=size(x);
%－ 一。首先要对样本进行聚类分析，以此来确定模糊规则个数。利用K-means法对样本聚类。

    ????此处的K－ means 法待加

%－ 二。建立模糊推理系统

%  隶属度函数个数-－模糊规则个数
   k=5;
%  初始化四个隶属度函数的参数A,B及输出层初始权值W

for i=1:p1; 
    for j=1:k;
     m(i,j)=1+0.6*rands(1);
     b(i,j)=1+0.6*rands(1);
    end
end
for  j=1:k;
     w(j)=100+rand(1);
end
%%%---推理计算输出值
for q=1:p2;
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%-----用同一隶属度参数对 输入样本 X 累计计算
 %  选用高斯函数作为隶属度，求隶属度，共 size(x,2)+k 个。x(1) K个，x(2) K个
 for i=1:p1;
    for j=1:k;
       u(i,j)=gaussmf(x(i,q),[m(i,j),b(i,j)]);
    end
 end
 %  模糊推理计算：a21,a22.几个隶属度函数，得出几个值，本例中两个.
   %%%%----由以前的取小做法改为相乘—prod(x,1) or prod(x,2)———
 for i=1:k;
    v(i)=1;
    j=1;
   while  j<=p1;
         v(i)=v(i)*u(j,i);
         j=j+1;
   end
 end
%  归一化计算模糊推理的值；相当于已经除去了经典去模糊输出的分母值
%for i=1:k;
    %a3(i)=a2(i)/sum(a2);
    %end
%  系统输出
out1(q)=w*v';
e(q)=(y(q)-out1(q));
end
out=out1


%－ 三。参数修正过程。         增加方式，非批处理方式迭代
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%－－－－－－－－---------------------误差反向传播过程－－－-----------------------------------------
% 取误差函数：E＝(1/2)*sumsqr(t-y)
E=(1/2)*sumsqr(y-out)
EE=E;
e
% e=sum(y-out)
lr=0.3;  % c2=zeros(2,2);
%%%%----------------------------------------误差反传后的参数修正过程-------------------
r=1;
p=1;
s=1000;
while p<=s & EE>1e-10
    %%%%%%%%%%%%%_____隶属度参数 M. B 输出层权值参数 W 的修正过程_____%%%%%%%%%%%%
   %%1.--W
   wc=zeros(1,k);
   for i=1:k;
    wc(i)=lr*e(r)*v(i);
   end
   %%2.--M
   mc=zeros(p1,k);
   for i=1:p1;
      for j=1:k;
        mc(i,j)=2*lr*e(r) * w(j) * (v(j)/u(i,j)) * exp(-((x(i,r)-m(i,j)).^2)/(b(i,j).^2))* (x(i,r)-m(i,j))/(b(i,j).^2);
      end
    end
   %%3.--B
   bc=zeros(p1,k);
   for i=1:p1;
      for j=1:k;
        bc(i,j)=2*lr*e(r)* w(j) * (v(j)/u(i,j)) * exp(-((x(i,r)-m(i,j)).^2)/(b(i,j).^2)) * ((x(i,r)-m(i,j)).^2)/(b(i,j).^3);
      end
   end
  % 4.参数修正  m b w
    m=m+mc;
    b=b+bc;
    w=w+wc;
        %%%%%%%%%%%_______利用修正后的参数重新计算_____________%%%%%%%%%%%%%%%%%%%%%
  % 5.利用修正过的参数重新计算输出
 for q=1:p2;  
   for i=1:p1;
      for j=1:k;
        u(i,j)=gaussmf(x(i,q),[m(i,j),b(i,j)]);
      end
   end
   for i=1:k;
       v(i)=1;
       j=1;
     while  j<=p1;
         v(i)=v(i)*u(j,i);
         j=j+1;
     end
   end
   out1(q)=w*v';
 end
 out=out1;
 p=p+1;
 EE=(1/2)*sumsqr(y-out);
 E(p)=EE;
 r=r+1;
 if  r>p2
     r=1;
 end
 e(r)=(y(r)-out(r));
end
%%%%%%%%%%%%%%%%%%%________________当误差或迭代步数满足要求后得到结果_________________%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
m,b,w,E_out=EE,e
epoch=1:size(E,2);
figure
plot(epoch,E,'-r');
axis([0 1.5*s min(E) max(E)]);
set(gca,'fontsize',8);
set(gca,'xtick',0:s/10:1.5*s);
%set(gca,'ytick',1e-30:1e5:1e5);
%set(gcf,'color','b')
title('误差变化曲线');xlabel('步数');ylabel('误差');
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%%%%%%%%%%%%%%%%%%%%%_________________________下一步就是模型仿真过程，即泛化