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Neuro-fuzzy modeling of complex systems using genetic algorithms

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3 Author(s)
Farag, W.A. ; Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada ; Quintana, V.H. ; Lambert-Torres, G.

In this paper, a genetic-based neuro-fuzzy approach is proposed to build and optimize fuzzy models. The learning algorithm of the fuzzy-neural network is divided into three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed to find the linguistic fuzzy rules. In the third phase, a new technique is used to apply a genetic algorithm to tune the membership functions of the fuzzy model optimally. A well-known example is used to investigate the performance of the proposed modeling approach, and compare it with the other modeling approaches

Published in:

Neural Networks,1997., International Conference on  (Volume:1 )

Date of Conference:

9-12 Jun 1997