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Proceedings Paper

Genetic algorithms for learning and design of optimal fuzzy trackers
Author(s): Wen-Ruey Hwang; Wiley E. Thompson
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Paper Abstract

A methodology for combining genetic algorithms (GA) and fuzzy algorithms for learning and design of optimal fuzzy trackers is presented. With the aid of genetic algorithms, optimal rules of fuzzy logic controllers and membership functions can be designed without human operator's experience and/or control engineer's knowledge. The approach presented here involves searching the decoded parameters of the membership functions and finding the optimal control rules based upon a fitness value which is defined in terms of a performance criterion. Two applications are presented: the first application deals with a GA that adjusts the fuzzy tracker at run-time on the basis of performance indices, and the second application deals with a Model Reference Adaptive Algorithm which is based on a crisp model of the closed loop system. The GA changes the parameters of the fuzzy tracker and the fuzzy membership functions in such a way that the closed loop system behaves like the reference model.

Paper Details

Date Published: 5 July 1995
PDF: 9 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213013
Show Author Affiliations
Wen-Ruey Hwang, Tennessee State Univ. (United States)
Wiley E. Thompson, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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