Matlab code works with one version but not the other

Question

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?

The error message that i am getting is "Parse error at ']' at lines 49, 130 188 & 189"

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=GreatPsiBigeye(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

Answer 1

Lines 49, 130, 188, and 190 are pasted below. Notice the trend?

[phi,~]=GetMePhi(x,c,sigma);
[M,~]=size(GreatPsiBig);
[~,u]=size(c);
[~,size_N]=size(X)

Basically as of MATLAB 2009b, the tilde operator was instituted so that variables that you didn't use later didn't have to be stored in memory. It also means that any code with tildes in it won't be backwards compatible before that version.