inv_co.m

The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB lan

function [SSE,par,bias]=inv_co(inputs,centers,bases,output)

%INV_CO		inversion on the consequents for constant_output_models
%
%  [norma,par,bias]=inv_co(inputs,centers,bases,output)
%
%	SSE[1,1]			Sum of the squeared errors
%	par[no_rules,1]			parameters of the consequents
%	bias[1,1]			shifts all the local models
%
%	inputs[no_data,no_var]
%	centers[no_rules,no_var]
%	bases[no_rules,no_var]
%	output[no_data,1]
%
%	The following global variable are required:                     
%		memb_fun:	'gaussian'/'triangular'                
% 		arch:		'weigthed'/'comb'                      
%		format_out:	'linear'/'constant'                       
%		model_bias:	'bias'/'no_bias'

%_______________________________________________________9 April 1996____


global memb_fun;
global arch;
global format_out;
global model_bias;


[no_data,no_var]=size(inputs);
[no_rules,no_var]=size(centers);

w=ones(no_data,no_rules);	%Matrix of the degree of match of each
				%datum to the premise of each rule
Sum_w=zeros(no_data,1);

for i=1:no_rules
	for j=1:no_var
		if strcmp(memb_fun,'gaussian')
			match=exp(-((inputs(:,j)-centers(i,j)).^2)/bases(i,j));
		end

		if strcmp(memb_fun,'triangular')
			match=max(0,ones(no_data,1)-2*abs(inputs(:,j)-centers(i,j))/bases(i,j));
		end

		w(:,i)=w(:,i).*match;
	end

	Sum_w=Sum_w + w(:,i);
end

Sum_w=Sum_w+(Sum_w==0);


if strcmp(arch,'comb')
	X=w;
end

if strcmp(arch,'weigthed')
	normalizer=Sum_w*ones(1,no_rules);
	X=w./normalizer;
end


if strcmp(model_bias,'bias')
	A=[X,ones(no_data,1)];
	tol=1e-9;
	
	par_and_bias=pinv(A,tol)*output;
	
	par=par_and_bias(1:no_rules,:);
	bias=par_and_bias(no_rules+1,:);
end



if strcmp(model_bias,'no_bias')
	A=X;
	tol=1e-9;

	par=pinv(A,tol)*output;
	bias=0;
end



SSE=sumsqr(output-X*par-bias);