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EvoNF: a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation

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1 Author(s)
A. Abraham ; Sch. of Bus. Syst., Monash Univ., Clayton, Vic., Australia

Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm will converge and the tuning of fuzzy inference system will be successful. Success of evolutionary search procedures for the optimization of fuzzy inference system is well proven and established in many application areas. In this paper, we explore how the optimization of fuzzy inference systems can be further improved using a meta-heuristic approach combining neural network learning and evolutionary computation. The proposed technique can be considered as a methodology to integrate neural networks, fuzzy inference systems and evolutionary search procedures. We present the theoretical frameworks and some experimental results to demonstrate the efficiency of the proposed technique.

Published in:

Intelligent Control, 2002. Proceedings of the 2002 IEEE International Symposium on

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