problem with RLS estimation of TS model

salutations

im trying to get the recursive least squares estimation of TS fuzzy model consequents. i have developed the following code but unfortunately and to my amazement, its generating mtimes error. unable to troubleshoot. the error appears at statement w=w'+K*epsilon, using teh option of element by elememt multiplication has also been proved useless as its giving times error:( can anyone resolve the issue.

plz verify my selection of regressor and parameter matrices (a and w respectively)

{
function [ output_args ] = RLS_Function( input_args )
%RLS_FUNCTION Summary of this function goes here
%   Detailed explanation goes here

x1=input_args(1);
x2=input_args(1);

% number_of_rules=5;
number_of_rules_mine=25;

%---------------------------------------------
%--------Initializing weights-----------------
%---------------------------------------------

persistent w;
if isempty(w)
    w=ones(3,number_of_rules_mine)*0.1;
end

% w=ones(1,number_of_rules)*0.1;

%---------------------------------------------
%-----------Initializing P---------------------
%---------------------------------------------

Pt=eye(25); % P(t)

%---------------------------------------------
%-----------Identification Process------------
%---------------------------------------------

% 
% for number_of_epochs=1:100
% % 
% for sample=1:101

          no_x1_mf=5;
          no_x2_mf=5;

          %---------------------------------------------
          %-----------Fuzzification --------------------
          %---------------------------------------------

          PossIx1=Fuzzification_x(x1);
          PossIx2=Fuzzification_x(x2);

          %---------------------------------------------
          %--------Calculating Strength of the rule-----
          %---------------------------------------------
  %         count=1;
          for i=1:3
              for j=1:number_of_rules_mine
                  if i==1
                  a(i,j)=PossIx1(i);  % dim(a) = 1 x 25 
                  end
                  if i==2
                  a(i,j)=PossIx2(i);  % dim(a) = 1 x 25 
                  end
                  if i==3
                  a(i,j)=1;  % dim(a) = 1 x 25 
                  end
  %                 a(count)=PossIx1(i);  % dim(a) = 1 x 25 
  %                 count=count+1;
              end
          end

          %count=total number of rules

          %---------------------------------------------
          %-----------Calculating K---------------------
          %---------------------------------------------

          num=Pt*a';
          den=eye(3)+a*Pt*a';

          K=num*inv(den);

          %---------------------------------------------
          %-----------Calculating P---------------------
          %---------------------------------------------

          Pt=Pt-K*a*Pt;

          %---------------------------------------------
          %------Calculating epsilon--------------------
          %---------------------------------------------

          epsilon=x1-a*w';  %since y=x

          %---------------------------------------------
          %------------Weight update--------------------
          %---------------------------------------------

          w=w'+K*epsilon;
          output_args=a*w';
  % end
  % end
      }

Answer 1

[first]

sorry i made some mistake in the code. since im dealing with TS fuzzy model of first order so the code should be as follows

{  
function [ output_args ] = RLS_Function( input_args )
%RLS_FUNCTION Summary of this function goes here
%   Detailed explanation goes here

x1=input_args(1);
x2=input_args(1);

number_of_rules=5;

%---------------------------------------------
%--------Initializing weights-----------------
%---------------------------------------------

persistent w;
if isempty(w)
    w=ones(3,number_of_rules*number_of_rules)*0.1;
end

% w=ones(1,number_of_rules)*0.1;

%---------------------------------------------
%-----------Initializing P---------------------
%---------------------------------------------

Pt=eye(25); % P(t)

%---------------------------------------------
%-----------Identification Process------------
%---------------------------------------------

% 
% for number_of_epochs=1:100
% % 
% for sample=1:101

          no_x1_mf=5;
          no_x2_mf=5;

          %---------------------------------------------
          %-----------Fuzzification --------------------
          %---------------------------------------------

          PossIx1=Fuzzification_x(x1);
          PossIx2=Fuzzification_x(x2);

          %---------------------------------------------
          %--------Calculating Strength of the rule-----
          %---------------------------------------------
          count=1;
          for i=1:no_x1_mf
              for j=1:no_x2_mf
                  a(count)=PossIx1(i)*PossIx2(j);  % dim(a) = 1 x 25 
  %                 a(count)=PossIx1(i);  % dim(a) = 1 x 25 
                  count=count+1;
              end
          end

          %count=total number of rules

          %---------------------------------------------
          %-----------Calculating K---------------------
          %---------------------------------------------

          num=Pt*a';
          den=1+a*Pt*a';

          K=num/den;

          %---------------------------------------------
          %-----------Calculating P---------------------
          %---------------------------------------------

          Pt=Pt-K*a*Pt;

          %---------------------------------------------
          %------Calculating epsilon--------------------
          %---------------------------------------------

          epsilon=x1-a*w'*[x2 x1 1]';  %since y=x
  %          epsilon=x1-a*w';

          %---------------------------------------------
          %------------Weight update--------------------
          %---------------------------------------------

          w=w'*[x2 x1 1]'+K*epsilon;
          output_args=a*w';
  end
  }