2022年自适应模糊神经网络MATLAB代码 .pdf

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1、function c, sigma , W_output = SOFNN( X, d, Kd ) %SOFNN Self-Organizing Fuzzy Neural Networks %Input Parameters % X(r ,n) - rth traning data from nth observation % d(n) - the desired output of the network (must be a row vector) % Kd(r) - predefined distance threshold for the rth input %Output Parame

2、ters % c(IndexInputVariable,IndexNeuron) % sigma(IndexInputVariable,IndexNeuron) % W_output is a vector %Setting up Parameters for SOFNN SigmaZero=4; delta=0.12; threshold=0.1354; k_sigma=1.12; %For more accurate results uncomment the following%format long; %Implementation of a SOFNN model size_R,si

3、ze_N=size(X); %size_R - the number of input variables c=; sigma=; W_output=; u=0; % the number of neurons in the structure Q=; O=; Psi=; for n=1:size_N x=X(:,n); if u=0 % No neuron in the structure? c=x; sigma=SigmaZero*ones(size_R,1); u=1; Psi=GetMePsi(X,c,sigma); Q,O = UpdateStructure(X,Psi,d); pT

4、_n=GetMeGreatPsi(x,Psi(n,:); else Q,O,pT_n = UpdateStructureRecursively(X,Psi,Q,O,d,n);end; 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 1 页，共 5 页 - - - - - - - - - KeepSpinning=true; while KeepSpinning %Calculate the error and if-part criteria ae=abs(d(n)-pT_n*O);

5、 %approximation error phi,=GetMePhi(x,c,sigma); maxphi,maxindex=max(phi); % maxindex refers to the neurons index if aedelta if maxphithreshold %enlarge width minsigma,minindex=min(sigma(:,maxindex);sigma(minindex,maxindex)=k_sigma*minsigma;Psi=GetMePsi(X,c,sigma); Q,O = UpdateStructure(X,Psi,d); pT_

6、n=GetMeGreatPsi(x,Psi(n,:); else %Add a new neuron and update structure ctemp=; sigmatemp=; dist=0; for r=1:size_R dist=abs(x(r)-c(r,1); distIndex=1; for j=2:u if abs(x(r)-c(r,j)dist distIndex=j; dist=abs(x(r)-c(r,j); end; end; if dist=Kd(r) ctemp=ctemp; c(r,distIndex); sigmatemp=sigmatemp ; sigma(r

7、,distIndex); else ctemp=ctemp; x(r); sigmatemp=sigmatemp ; dist; end; end; c=c ctemp; sigma=sigma sigmatemp; Psi=GetMePsi(X,c,sigma); Q,O = UpdateStructure(X,Psi,d); KeepSpinning=false; u=u+1; end; 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 2 页，共 5 页 - - - - - -

8、- - - else if maxphi=ae L=Q*p_n*(temp)(-1); Q_next=(eye(length(Q)-L*pT_n)*Q; O_next=O + L*ee; else Q_next=eye(length(Q)*Q; O_next=O; end; end function Q , O = UpdateStructure(X,Psi,d) GreatPsiBig = GetMeGreatPsi(X,Psi); %M=u*(r+1) %n - the number of observations M,=size(GreatPsiBig); %Others Ways of

9、 getting Q=PT(t)*P(t)-1 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 3 页，共 5 页 - - - - - - - - - %* %opts.SYM = true; %Q = linsolve(GreatPsiBig*GreatPsiBig,eye(M),opts); % %Q = inv(GreatPsiBig*GreatPsiBig); %Q = pinv(GreatPsiBig*GreatPsiBig); %* Y=GreatPsiBigeye(M)

10、; Q=GreatPsiBigY; O=Q*GreatPsiBig*d; end %This function works too with x % (X=X and Psi is a Matrix) - Gets you the whole GreatPsi % (X=x and Psi is the row related to x) - Gets you just the column related with the observation function GreatPsi = GetMeGreatPsi(X,Psi) %Psi - In a row you go through t

11、he neurons and in a column you go through number of %observations * Psi(#obs,IndexNeuron) * GreatPsi=; N,U=size(Psi); for n=1:N x=X(:,n); GreatPsiCol=; for u=1:U GreatPsiCol= GreatPsiCol ; Psi(n,u)*1; x ;end; GreatPsi=GreatPsi GreatPsiCol; end; end function phi, SumPhi=GetMePhi(x,c,sigma) r,u=size(c

12、); %u - the number of neurons in the structure %r - the number of input variables phi=; SumPhi=0; 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 4 页，共 5 页 - - - - - - - - - for j=1:u % moving through the neurons S=0; for i=1:r % moving through the input variables S =

13、 S + (x(i) - c(i,j)2) / (2*sigma(i,j)2); end; phi = phi exp(-S); SumPhi = SumPhi + phi(j); %phi(u)=exp(-S) end; end %This function works too with x, it will give you the row related to x function Psi = GetMePsi(X,c,sigma) ,u=size(c); ,size_N=size(X); %u - the number of neurons in the structure %size

14、_N - the number of observations Psi=; for n=1:size_N phi, SumPhi=GetMePhi(X(:,n),c,sigma); PsiTemp=; for j=1:u %PsiTemp is a row vector ex: 1 2 3 PsiTemp(j)=phi(j)/SumPhi; end; Psi=Psi; PsiTemp; %Psi - In a row you go through the neurons and in a column you go through number of %observations * Psi(#obs,IndexNeuron) * end; end名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 5 页，共 5 页 - - - - - - - - -