Optimization of fuzzy expert systems using genetic algorithms and neural networks

Author

Perneel, Christiaan ; Themlin, Jean-Marc ; Renders, Jean-Michel ; Acheroy, Marc

Author_Institution

Signal & Image Center, R. Mil. Acad., Brussels, Belgium

Volume

3

Issue

3

fYear

1995

fDate

8/1/1995 12:00:00 AM

Firstpage

300

Lastpage

312

Abstract

In this paper, fuzzy logic theory is used to build a specific decision-making system for heuristic search algorithms. Such algorithms are typically used for expert systems. To improve the performance of the overall system, a set of important parameters of the decision-making system is identified. Two optimization methods for the learning of the optimum parameters, namely genetic algorithms and gradient-descent techniques based on a neural network formulation of the problem, are used to obtain an improvement of the performance. The decision-making system and both optimization methods are tested on a target recognition system

Keywords

decision theory; expert systems; fuzzy logic; genetic algorithms; neural nets; optimisation; decision-making system; fuzzy expert systems; fuzzy logic theory; genetic algorithms; gradient-descent techniques; heuristic search algorithms; neural networks; target recognition system; Decision making; Expert systems; Fuzzy logic; Genetic algorithms; Heuristic algorithms; Hybrid intelligent systems; Neural networks; Optimization methods; System testing; Target recognition;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.413235

Filename

413235