自适应模糊神经网络MATLAB代码.doc

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 SOFNNSigmaZero=4;delta=0.12;threshold=0.1354;k_sigma=1.12;%For more accurate results uncomment the following%format long;%Implementation of a SOFNN modelsize_R,size_N=size(X);%size_R - the number of input variablesc=;sigma=;W_output=;u=0; % the number of neurons in the structureQ=;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 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_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=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;endfunction Q , O = UpdateStructure(X,Psi,d)GreatPsiBig = GetMeGreatPsi(X,Psi);%M=u*(r+1)%n - the number of observationsM,=size(GreatPsiBig);%Others Ways of getting Q=PT(t)*P(t)-1%*%opts.SYM = true;%Q = linsolve(GreatPsiBig*GreatPsiBig,eye(M),opts);%Q = inv(GreatPsiBig*GreatPsiBig);%Q = pinv(GreatPsiBig*GreatPsiBig);%*Y=GreatPsiBigeye(M);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 observationfunction 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;endfunction phi, SumPhi=GetMePhi(x,c,sigma)r,u=size(c);%u - the number of neurons in the structure%r - the number of input variablesphi=;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 xfunction 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 observationsPsi=;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; en