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Radial basis function approximation

Radial basis function approximation. K-mean Algorithm. function y=my_kmeans(X,M) mx =mean(X); Y=rand(M,2)*0.1-0.05+mx; change =1; wh ile change < 10.^-6 D = cross_distances(X,Y) [xx ind]=min(D'); for i=1:K ind2=find(ind == i); Y_new(i,:) = mean( X( ind2,:));

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Lecture 5II

Lecture 5II. Roots of nonlinear functions Nested FOR loops. Nonlinear system. A set of nonlinear functions. Function evaluation. function F = myfun(x) F(1) = x(1).^2 + x(2)^2-4; F(2) = x(1) - x(2); return. x=linspace(-2,2); plot(x,x); hold on; plot(x,sqrt(4-x.^2))

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Radial basis function approximation

Lecture 5II