The design of beta basis function neural network and beta fuzzy systems by a hierarchical genetic algorithm

Authors:
Chaouki Aouiti;Adel M. Alimi;Fakhreddine Karray;Aref Maalej
Affiliations:
University of 7 November (Carthage), Faculty of Sciences of Bizerta, BP W - 7021, Bizerta, Tunisia;Department of Electrical Engineering, REGIM: Research Group on Intelligent Machines, University of Sfax, ENIS, BP W - 3038, Sfax, Tunisia;PAMI: Pattern Analysis and Machine Intelligence Laboratory, Department of Electrical and Computer Engineering, University of Waterloo, Ont., Canada N2L 3G1;LASEM: Laboratory of Electromechanical Systems University of Sfax, ENIS, Department of Mechanical Engineering, BP W - 3038, Sfax, Tunisia
Venue:
Fuzzy Sets and Systems
Year:
2005

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Abstract

We propose an evolutionary method for the design of beta basis function neural networks (BBFNN) and of beta fuzzy systems (BFS). Classical training algorithms start with a predetermined network structure for neural networks and with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking both the neural network and the fuzzy systems are either insufficient or overcomplicated. This paper describes a hierarchical genetic learning model of the BBFNN and the BFS. In order to examine the performance of the proposed algorithm, it is used for functional approximation problem for the case of BBFNN and for the identification of an induction machine fuzzy plant model for the case of BFS. The results obtained have been encouraging.