I have a code that works in Matlab version R2010a but shows errors in matlab R2008a. I am trying to implement a self organizing fuzzy neural network with extended kalman filter. I have the code running but it only works in matlab version R2010a. It doesn't work with other versions. Any help? Code attach 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 Parameters % 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,size_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_n=GetMeGreatPsi(x,Psi(n,:))'; else [Q,O,pT_n] = UpdateStructureRecursively(X,Psi,Q,O,d,n); end; KeepSpinning=true; while KeepSpinning %Calculate the error and if-part criteria ae=abs(d(n)-pT_n*O); %approximation error [phi,~]=GetMePhi(x,c,sigma); [maxphi,maxindex]=max(phi); % maxindex refers to the neuron's index if ae>delta if maxphi<threshold %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_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,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; else if maxphi<threshold %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_n=GetMeGreatPsi(x,Psi(n,:))'; else %Do nothing and exit the while KeepSpinning=false; end; end; end; end; W_output=O; end function [Q_next, O_next,pT_n] = UpdateStructureRecursively(X,Psi,Q,O,d,n) %O=O(t-1) O_next=O(t) p_n=GetMeGreatPsi(X(:,n),Psi(n,:)); pT_n=p_n'; ee=abs(d(n)-pT_n*O); %|e(t)| temp=1+pT_n*Q*p_n; ae=abs(ee/temp); if ee>=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 getting Q=[P^T(t)*P(t)]^-1 %************************************************************************** %opts.SYM = true; %Q = linsolve(GreatPsiBig*GreatPsiBig',eye(M),opts); % %Q = inv(GreatPsiBig*GreatPsiBig'); %Q = pinv(GreatPsiBig*GreatPsiBig'); %************************************************************************** Y=GreatPsiBig\eye(M); Q=GreatPsiBig'\Y; 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 the 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); %u - the number of neurons in the structure %r - the number of input variables phi=[]; SumPhi=0; for j=1:u % moving through the neurons S=0; for i=1:r % moving through the input variables S = 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_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