自适应模糊神经网络MATLAB代码Word下载.docx

1、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 stru

2、cture?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,:）; elseQ,O,pT_n = UpdateStructureRecursively（X,Psi,Q,O,d,n）; end; KeepSpinning=true; while KeepSpinning%Calculate the error and if-part criteriaae=abs（d（n）-pT_n*O）; %approx

3、imation errorphi,=GetMePhi（x,c,sigma）;maxphi,maxindex=max（phi）; % maxindex refers to the neurons indexif aedeltaif 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=GetMeGre

4、atPsi（x,Psi（n,:else %Add a new neuron and update structure ctemp=; sigmatemp=; dist=0; for r=1:size_Rdist=abs（x（r）-c（r,1）;distIndex=1;for j=2:uif abs（x（r）-c（r,j）dist distIndex=j; dist=abs（x（r）-c（r,j）;end;if dist=ae L=Q*p_n*（temp）（-1）; Q_next=（eye（length（Q）-L*pT_n）*Q; O_next=O + L*ee; Q_next=eye（leng

5、th（Q）*Q; O_next=O;function 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

6、 = pinv（GreatPsiBig*GreatPsiBigY=GreatPsiBigeye（M）;Q=GreatPsiBigY;O=Q*GreatPsiBig*d%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）;N GreatPsiCol=; for u=1:UGreatPsiCol= GreatPsiCol ; Psi（n,u）*1; x ; GreatPsi=GreatPsi GreatPsiCol;function phi, SumPhi=GetM