Genetic algorithms for learning and design of optimal fuzzy trackers

Wen-Ruey Hwang; Wiley E. Thompson

doi:10.1117/12.213013

5 July 1995 Use of genetic algorithms for learning and design of optimal fuzzy trackers

Wen-Ruey Hwang, Wiley E. Thompson

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213013
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Wen-Ruey Hwang and Wiley E. Thompson "Use of genetic algorithms for learning and design of optimal fuzzy trackers", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213013

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