I. Kallel, A.M. Alimi, and K. Ghédira (Tunisia)
Induction Machine - Beta Fuzzy Systems - Learning Genetic algorithms - Multi-Agent Systems - Distributed Genetic Algorithms
This paper proposes a learning method for Beta fuzzy systems (BFS) based on a multi-agent genetic algorithm. This method, called MAGAD-BFS (Multi-Agent Genetic Algorithm for the Design of BFS) has tow advantages. First, thanks to genetic algorithms (GA) efficiency, it allows to design a suitable and precise model for Beta fuzzy systems. Second, it improves the GA convergence by reducing rule complexity thanks to the distributed implementation by multi-agent approach. Dynamic agents interact to provide an optimal solution in order to obtain the best BFS reaching the balance interpretability precision. The performance of the method is tested on a simulated example about an automatic identification of an induction machine fuzzy plant model.
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