📄 模糊神经.html
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"><html xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <!--This HTML is auto-generated from an M-file.To make changes, update the M-file and republish this document. --> <title>模糊神经</title> <meta name="generator" content="MATLAB 7.4"> <meta name="date" content="2009-05-12"> <meta name="m-file" content="模糊神经"><style>body { background-color: white; margin:10px;}h1 { color: #990000; font-size: x-large;}h2 { color: #990000; font-size: medium;}/* Make the text shrink to fit narrow windows, but not stretch too far in wide windows. */ p,h1,h2,div.content div { max-width: 600px; /* Hack for IE6 */ width: auto !important; width: 600px;}pre.codeinput { background: #EEEEEE; padding: 10px;}@media print { pre.codeinput {word-wrap:break-word; width:100%;}} span.keyword {color: #0000FF}span.comment {color: #228B22}span.string {color: #A020F0}span.untermstring {color: #B20000}span.syscmd {color: #B28C00}pre.codeoutput { color: #666666; padding: 10px;}pre.error { color: red;}p.footer { text-align: right; font-size: xx-small; font-weight: lighter; font-style: italic; color: gray;} </style></head> <body> <div class="content"><pre class="codeinput">clearclctic,<span class="comment">%[x,y]=data;</span>x=[1 1 1;1 2 3];y=[2 3 4]; <span class="comment">%%%%%--数据显示，输入为－两输入，输出为－单输出。--------样本为p2组</span>[p1,p2]=size(x);<span class="comment">%－ 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。</span><span class="comment">%????此处的K－ means 法待加</span><span class="comment">%－ 二。建立模糊推理系统</span><span class="comment">% 隶属度函数个数-－模糊规则个数</span>k=5;<span class="comment">% 初始化四个隶属度函数的参数A,B及输出层初始权值W</span><span class="keyword">for</span> i=1:p1;<span class="keyword">for</span> j=1:k;m(i,j)=1+0.6*rands(1);b(i,j)=1+0.6*rands(1);<span class="keyword">end</span><span class="keyword">end</span><span class="keyword">for</span> j=1:k;w(j)=100+rand(1);<span class="keyword">end</span><span class="comment">%%%---推理计算输出值</span><span class="keyword">for</span> q=1:p2;<span class="comment">%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%-----用同一隶属度参数对 输入样本 X 累计计算</span><span class="comment">% 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</span><span class="keyword">for</span> i=1:p1;<span class="keyword">for</span> j=1:k;u(i,j)=gaussmf(x(i,q),[m(i,j),b(i,j)]);<span class="keyword">end</span><span class="keyword">end</span><span class="comment">% 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.</span><span class="comment">%%%%----由以前的取小做法改为相乘—prod(x,1) or prod(x,2)———</span><span class="keyword">for</span> i=1:k;v(i)=1;j=1;<span class="keyword">while</span> j<=p1;v(i)=v(i)*u(j,i);j=j+1;<span class="keyword">end</span><span class="keyword">end</span><span class="comment">% 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</span><span class="comment">%for i=1:k;</span><span class="comment">%a3(i)=a2(i)/sum(a2);</span><span class="comment">%end</span><span class="comment">% 系统输出</span>out1(q)=w*v';e(q)=(y(q)-out1(q));<span class="keyword">end</span>out=out1<span class="comment">%－ 三。参数修正过程。 增加方式，非批处理方式迭代</span><span class="comment">%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</span><span class="comment">%%%%%%%%%%%%－－－－－－－－---------------------误差反向传播过程－－－-----------------------------------------</span><span class="comment">% 取误差函数：E＝(1/2)*sumsqr(t-y)</span>E=(1/2)*sumsqr(y-out)EE=E;e<span class="comment">% e=sum(y-out)</span>lr=0.3; <span class="comment">% c2=zeros(2,2);</span><span class="comment">%%%%----------------------------------------误差反传后的参数修正过程-------------------</span>r=1;p=1;s=1000;<span class="keyword">while</span> p<=s & EE>1e-10<span class="comment">%%%%%%%%%%%%%_____隶属度参数 M. B 输出层权值参数 W 的修正过程_____%%%%%%%%%%%%</span><span class="comment">%%1.--W</span>wc=zeros(1,k);<span class="keyword">for</span> i=1:k;wc(i)=lr*e(r)*v(i);<span class="keyword">end</span><span class="comment">%%2.--M</span>mc=zeros(p1,k);<span class="keyword">for</span> i=1:p1;<span class="keyword">for</span> 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);<span class="keyword">end</span><span class="keyword">end</span><span class="comment">%%3.--B</span>bc=zeros(p1,k);<span class="keyword">for</span> i=1:p1;<span class="keyword">for</span> 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);<span class="keyword">end</span><span class="keyword">end</span><span class="comment">% 4.参数修正 m b w</span>m=m+mc;b=b+bc;w=w+wc;<span class="comment">%%%%%%%%%%%_______利用修正后的参数重新计算_____________%%%%%%%%%%%%%%%%%%%%%</span><span class="comment">% 5.利用修正过的参数重新计算输出</span><span class="keyword">for</span> q=1:p2;<span class="keyword">for</span> i=1:p1;<span class="keyword">for</span> j=1:k;u(i,j)=gaussmf(x(i,q),[m(i,j),b(i,j)]);<span class="keyword">end</span><span class="keyword">end</span><span class="keyword">for</span> i=1:k;v(i)=1;j=1;<span class="keyword">while</span> j<=p1;v(i)=v(i)*u(j,i);j=j+1;<span class="keyword">end</span><span class="keyword">end</span>out1(q)=w*v';<span class="keyword">end</span>out=out1;p=p+1;EE=(1/2)*sumsqr(y-out);E(p)=EE;r=r+1;<span class="keyword">if</span> r>p2r=1;<span class="keyword">end</span>e(r)=(y(r)-out(r));<span class="keyword">end</span><span class="comment">%%%%%%%%%%%%%%%%%%%________________当误差或迭代步数满足要求后得到结果_________________%%%%%%</span><span class="comment">%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</span>m,b,w,E_out=EE,eepoch=1:size(E,2);figureplot(epoch,E,<span class="string">'-r'</span>);axis([0 1.5*s min(E) max(E)]);set(gca,<span class="string">'fontsize'</span>,8);set(gca,<span class="string">'xtick'</span>,0:s/10:1.5*s);<span class="comment">%set(gca,'ytick',1e-30:1e5:1e5);</span><span class="comment">%set(gcf,'color','b')</span>title(<span class="string">'误差变化曲线'</span>);xlabel(<span class="string">'步数'</span>);ylabel(<span class="string">'误差'</span>);toc</pre><pre class="codeoutput">out = 387.8237 354.7163 130.8296E = 1.4433e+005e = -385.8237 -351.7163 -126.8296m = 1.0e+004 * 0.0193 -0.1812 0.2562 -0.1324 0.2621 -0.3428 0.6440 1.5911 -0.0969 0.3025b = 1.0e+004 * -0.0047 -0.0541 -0.0786 -0.0332 -0.0922 -0.0899 -0.3594 -1.0052 -0.0059 -0.0682w = 21.2743 2.4621 17.7548 -4.5161 -32.5508E_out = 3.8667e = -2.1346 1.3826 0.7520Elapsed time is 2.312213 seconds.</pre><img vspace="5" hspace="5" src="%E6%A8%A1%E7%B3%8A%E7%A5%9E%E7%BB%8F_01.png"> <p class="footer"><br> Published with MATLAB® 7.4<br></p> </div> <!--##### SOURCE BEGIN #####%%
clear
clc
tic,
%[x,y]=data;
x=[1 1 1;
1 2 3];
y=[2 3 4]; %%%%%REPLACE_WITH_DASH_DASH数据显示，输入为－两输入，输出为－单输出。REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH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
%%%REPLACE_WITH_DASH_DASH-推理计算输出值
for q=1:p2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH-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.
%%%%REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%－－－－－－－－REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH-误差反向传播过程－－－REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH-
% 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);
%%%%REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH误差反传后的参数修正过程REPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASHREPLACE_WITH_DASH_DASH-
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.REPLACE_WITH_DASH_DASHW
wc=zeros(1,k);
for i=1:k;
wc(i)=lr*e(r)*v(i);
end
%%2.REPLACE_WITH_DASH_DASHM
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.REPLACE_WITH_DASH_DASHB
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
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