神经网络 神经网络中的一种，模糊神经网络的matlab实现算法

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'); toc