This paper presents a new method of using hybrid intelligent systems to solve the problem of tuning the parameters of a fuzzy logic controller. Two different hybrid intelligent systems are introduced in this paper. Each system proposes learning in a two-stage iterative process. The first system combines a fuzzy logic controller with genetic algorithms to form the iterative genetic based fuzzy logic controller technique (IGBFLC). The second system combines a fuzzy logic controller with an adaptive network to form the iterative adaptive network fuzzy inference system (IANFIS). The proposed systems are applied to a model of pursuit-evasion game. In this model, we are seeking for the optimal strategy of the pursuer given that the evader plays its optimal strategy. The proposed systems are compared with the PD controller, the Genetic-based fuzzy logic controller and the ANFIS technique. Computer simulations and results show that when compared to the optimal strategy, the proposed systems outperform the other techniques.

Additional Metadata
Keywords ANFIS, Fuzzy logic controller, Genetic algorithms, Hybrid intelligent system, Pursuit-evasion game
Persistent URL dx.doi.org/10.1109/ICSMC.2009.5346113
Conference 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
Citation
Desouky, S.F. (Sameh F.), & Schwartz, H.M. (2009). Hybrid intelligent systems applied to the pursuit-evasion game. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 2603–2608). doi:10.1109/ICSMC.2009.5346113