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Unformatted text preview: Fuzzy Membership Functions Fuzzy Operations Fuzzy Union Fuzzy Intersection Fuzzy Complement CSCI3406 Fuzzy Logic Week 3 (17/10/2005) Week 4 (24/10/2005) Some info for LAB Work on an mfile (open mfile for each task, write your programme, save the file (e.g., lab2task1), then execute the file. Now, this file has become a function in MATLAB) (see the first weeks slides  Week 1 ) Use help < help < function function > > (e.g., help newfis) If you dont know how to use the function. It gives you information about how to use the function and what parameters it requires Fuzzy Membership Functions One of the key issues in all fuzzy sets is how to determine fuzzy membership functions The membership function fully defines the fuzzy set A membership function provides a measure of the degree of similarity of an element to a fuzzy set Membership functions can take any form, but there are some common examples that appear in real applications Membership functions can either be chosen by the user arbitrarily, based on the users experience (MF chosen by two users could be different depending upon their experiences, perspectives, etc.) Or be designed using machine learning methods (e.g., artificial neural networks, genetic algorithms, etc.) There are different shapes of membership functions; triangular, trapezoidal, piecewiselinear, Gaussian, bellshaped, etc. Triangular membership function a, b and c represent the x coordinates of the three vertices of A ( x ) in a fuzzy set A (a: lower boundary and c: upper boundary where membership degree is zero, b: the centre where membership degree is 1)   = c x if c x b if b c x c b x a if a b a x a x if x A ) ( a b c x A ( x ) 1...
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This note was uploaded on 05/25/2011 for the course ECON 103 taught by Professor Poul during the Spring '11 term at American University of Central Asia.
 Spring '11
 poul

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