模糊神经网络，可自动调节模糊规则

function [w1,w2,width,rule,e,RMSE]=DFNN(p,t,parameters) % This is D_FNN trainning program. % Input: % p is the input data,which is r by q matrix. % r is the No. of input. % t is the output data,whic is s2 by q matrix. % q is the No. of sample data. % Output: % w1 is the centers of the RBF units,which is a u by r matrix. % w2 is the weights,which is s2 by u(1+r) matrix. % width is the widths of RBF units,which is a 1 by u matrix. if nargin<2 error('Not enough input arguments.') end if size(p,2)~=size(t,2) error('The input data are not correct.') end [r,q]=size(p);[s2,q]=size(t); %Setting predefined parameters kdmax=parameters(1);kdmin=parameters(2);gama=parameters(3); emax=parameters(4);emin=parameters(5);beta=parameters(6); width0=parameters(7);k=parameters(8);kw=parameters(9); kerr=parameters(10); ALLIN=[];ALLOUT=[];CRBF=[]; %When first sample data coming ALLIN=p(:,1);ALLOUT=t(:,1); %Setting up the initial DFNN CRBF=p(:,1);w1=CRBF';width(1)=width0;rule(1)=1; %Caculating the first out error a0=RBF(dist(w1,ALLIN),1./width'); a01=[a0 p(:,1)']; w2=ALLOUT/a01'; a02=w2*a01'; RMSE(1)=sqrt(sumsqr(ALLOUT-a02)/s2); %When other sample data coming for i=2:q IN=p(:,i);OUT=t(:,i); ALLIN=[ALLIN IN]; ALLOUT=[ALLOUT OUT]; [r,N]=size(ALLIN);[s,r]=size(w1); dd=dist(w1,IN); [d,ind]=min(dd); kd=max(kdmax*gama.^(i-1),kdmin); %Caculating the actual output of its sample data ai=RBF(dist(w1,IN),1./width'); ai=ai/sum(ai);ai1=transf(ai,IN); ai2=w2*ai1; errout=t(:,i)-ai2; errout=sum(errout.*errout)/s2; e(i)=sqrt(errout); ke=max(emax*beta.^(i-1),emin); if d>kd if e(i)>ke CRBF=[CRBF IN];%Add a new rule wb=k*d; width=[width wb]; w1=CRBF'; [u,v]=size(w1); %Calculating ouputs of RBF after growing for all data A=RBF(dist(w1,ALLIN),1./width'); A0=sum(A); A1=A./(ones(u,1)*A0); A2=transf(A1,ALLIN); if u*(r+1)<=N %Caculating error reduction rate tT=ALLOUT'; PAT=A2'; [W,AW]=orthogonalize(PAT); SSW=sum(W.*W)';SStT=sum(tT.*tT)'; err=((W'*tT)'.^2)./(SStT*SSW');errT=err'; err1=zeros(u,s2*(r+1)); err1(:)=errT;err21=err1'; err22=sum(err21.*err21)/(s2*(r+1)); err23=sqrt(err22); No=find(err23<kerr); if ~isempty(No) CRBF(:,No)=[];w1(No,:)=[]; width(:,No)=[];err21(:,No)=[]; [uv,vv]=size(w1); AA=RBF(dist(w1,ALLIN),1./width'); AA0=sum(AA); AA1=AA./(ones(uu,1)*AA0); AA2=transf(AA1,ALLIN); w2=ALLOUT/AA2; outAA2=w2*AA2; sse0=sumsqrt(ALLOUT-outAA2)/(i*s2); RMSE(i)=sqrt(sse0); rule(i)=uu; w2T=w2';ww2=zeros(uu,s2*(r+1)); ww2(:)=w2T; w21=ww2'; else w2=ALLOUT/A2; %w2 is s2*u(r+1) outA2=w2*A2; sse0=sumsqr(ALLOUT-outA2)/(s2*i); RMSE(i)=sqrt(sse0); rule(i)=u; w2T=w2';ww2=zeros(u,s2*(r+1)); ww2(:)=w2T; w21=ww2'; end else w2=ALLOUT/A2; outA2=w2*A2; sse0=sumsqr(ALLOUT-outA2)/(s2*i); RMSE(i)=sqrt(sse0); rule(i)=u; w2T=w2';ww2=zeros(u,s2*(r+1)); ww2(:)=w2T; w21=ww2'; end else a=RBF(dist(w1,ALLIN),1./width'); a0=sum(a);a1=a./(ones(s,1)*a0); a2=transf(a1,ALLIN); w2=ALLOUT/a2; outa2=w2*a2; sse1=sumsqr(ALLOUT-outa2)/(s2*i); RMSE(i)=sqrt(sse1); rule(i)=s; end else if e(i)>ke; width(ind)=kw*width(ind); aa=RBF(dist(w1,ALLIN),1./width'); aa0=sum(aa);aa1=aa./(ones(s,1)*aa0); aa2=transf(aa1,ALLIN); w2=ALLOUT/aa2; outaa2=w2*aa2; sse2=sumsqr(ALLOUT-outaa2)/(i*s2); RMSE(i)=sqrt(sse2); rule(i)=s; else aa1=RBF(dist(w1,ALLIN),1./width'); aa01=sum(aa1);aa11=aa1./(ones(s,1)*aa01); aa21=transf(aa11,ALLIN); w2=ALLOUT/aa21; outaa21=w2*aa21; sse3=sumsqr(ALLOUT-outaa21)/(s2*i); RMSE(i)=sqrt(sse3); rule(i)=s; end end end function[w,a]=orthogonalize(p) %This program is used to transform p into orthogonalize w [u,v]=size(p);w(:,1)=p(:,1);a=eye(v); for k=2:v b=zeros(u,1); for i=1:k-1 a(i,k)=w(:,1)'*p(:,k)/(w(:,i)'*w(:,i)); b=b+a(i,k)*w(:,i); end w(:,k)=p(:,k)-b; end