fuz_mod.m

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

function  out=fuz_mod(inputs,centers,bases,par,bias)

%FUZ_MOD
%
%  out=fuz_mod(inputs,centers,bases,par,bias)
%
%	out[no_data,1]   	
%
%	inputs[no_data,no_var]
%	centers[no_rules,no_var]   centers of the membership functions
%	bases[no_rules,no_var]	   bases   of the membership functions
%	par[no_rules,no_par]	   parameters of the consequent of each rule
%	bias[1,1]
% 
%	Given an input matrix and the matrices centers, bases, par and bias, 
%	fuz_mod returns the corresponding output of the fuzzy system
%   
%	The following global variable are required:                     
%		memb_fun:	'gaussian'/'triangular'                
% 		arch:		'weigthed'/'comb'                      
%		format_out:	'linear'/'constant'                       

%_______________________________________________________6 April 1996____

global memb_fun;
global arch;
global format_out;

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

	
if strcmp(format_out,'linear')
	y=[inputs,ones(no_data,1)]*par';
end

if strcmp(format_out,'constant')
	y=ones(no_data,1)*par';
end


Sum_w=zeros(no_data,1);
Sum_wy=zeros(no_data,1);

for i=1:no_rules
	
	w=ones(no_data,1);	%initialization of the degree of match 
			%of the inputs to the premise of the i-th rule

	for j=1:no_var
			%match[no_data,1] is the degree of match of each   
			%datum to the premise of the i-th rule for what  
			%concerns the j-th direction of the input space

	  	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=w.*match;
	end

	Sum_wy=Sum_wy + w.*y(:,i);
	Sum_w=Sum_w + w; 
end

Sum_w=Sum_w + (Sum_w==0);		%if Sum_w=0 then Sun_w=1
					% n.b. in this case Sum_wy=0
if strcmp(arch, 'weigthed')
	out=Sum_wy./Sum_w;
end

if strcmp(arch, 'comb')
	out=Sum_wy;
end

out=out+bias*ones(size(out));