TS.rar_TS Fuzzy_Takagi Sugeno_Takagi-Sugeno_fuzzy sugeno_sugeno

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % For training Takagi-Sugeno fuzzy systems using batch least squares % % By: Kevin Passino % Version: 1/26/99 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear % First, generate the training data, G % For the M=121 case x=-6:0.1:6; M=length(x) for i=1:M, z(i)=0.15*(rand-0.5)*2; % Define the auxiliary variable G(i)=exp(-50*(x(i)-1)^2)-0.5*exp(-100*(x(i)-1.2)^2)+atan(2*x(i))+2.15+... 0.2*exp(-10*(x(i)+1)^2)-0.25*exp(-20*(x(i)+1.5)^2)+0.1*exp(-10*(x(i)+2)^2)-0.2*exp(-10*(x(i)+3)^2); if x(i) >= 0 G(i)=G(i)+0.1*(x(i)-2)^2-0.4; end Gz(i)=G(i)+z(i); % Adds in the influence of the auxiliary variable % fpoly(i)=0.6+0.1*x(i); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Consider the R=20 case % First, form the vector Y Y=Gz'; % Next, find Phi, which involves processing x through phi R=20; c(1,:)=-5.4:0.6:6; sigma(1,:)=0.1*ones(1,R); % Initialize Phi for j=1:R mu(j,1)=exp(-0.5*((x(1)-c(1,j))/sigma(1,j))^2); end denominator(1)=sum(mu(:,1)); for j=1:R xi(j,1)=mu(j,1)/denominator(1); end Phi=[xi(:,1)', x(1)*xi(:,1)']; % Form the rest of Phi for i=2:M for j=1:R mu(j,i)=exp(-0.5*((x(i)-c(1,j))/sigma(1,j))^2); end denominator(i)=sum(mu(:,i)); for j=1:R xi(j,i)=mu(j,i)/denominator(i); end Phi=[Phi; xi(:,i)', x(i)*xi(:,i)']; end % Find the least squares estimate theta=Phi\Y theta40=theta; % This is for saving it for later use in other programs save variables theta40 % Note that we tested the result by plotting the resulting approximator % mapping and it produces a reasonable result. It is for this reason % that we trust the numerical computations, and do not seek to % use other methods for the computation of the estimate. % Next, compute the approximator values for i=1:M, phi=[xi(:,i)', x(i)*xi(:,i)']'; Fts(i)=theta'*phi; end % Next, plot the basis functions, data and the approximator to compare figure(1) plot(x,xi,'k') xlabel('x') ylabel('Basis function values') title('Takagi-Sugeno fuzzy system, 20 rules, basis functions') grid figure(2) plot(x,Gz,'ko',x,Fts,'k') xlabel('x(i)') ylabel('y(i)=G(x(i),z(i)), and fuzzy system output') title('Takagi-Sugeno fuzzy system, 20 rules') grid axis([min(x) max(x) 0 max(G)]) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Next, consider a different value for sigma Y=Gz'; % Next, find Phi, which involves processing x through phi clear Phi sigma(1,:)=1*ones(1,R); % Initialize Phi for j=1:R mu(j,1)=exp(-0.5*((x(1)-c(1,j))/sigma(1,j))^2); end denominator(1)=sum(mu(:,1)); for j=1:R xi(j,1)=mu(j,1)/denominator(1); end Phi=[xi(:,1)', x(1)*xi(:,1)']; % Form the rest of Phi for i=2:M for j=1:R mu(j,i)=exp(-0.5*((x(i)-c(1,j))/sigma(1,j))^2); end denominator(i)=sum(mu(:,i)); for j=1:R xi(j,i)=mu(j,i)/denominator(i); end Phi=[Phi; xi(:,i)', x(i)*xi(:,i)']; end % Find the least squares estimate theta=Phi\Y % Next, compute the approximator values for i=1:M, phi=[xi(:,i)', x(i)*xi(:,i)']'; Fts(i)=theta'*phi; end % Next, plot the basis functions, data and the approximator to compare figure(3) plot(x,xi,'k') xlabel('x') ylabel('Basis function values') title('Takagi-Sugeno fuzzy system, 20 rules, basis functions') grid figure(4) plot(x,Gz,'ko',x,Fts,'k') xlabel('x(i)') ylabel('y(i)=G(x(i),z(i)), and fuzzy system output') title('Takagi-Sugeno fuzzy system, 20 rules') grid axis([min(x) max(x) 0 max(G)]) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % End of program %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%