A new approach to evolutionary optimized fuzzy neural networks

Authors:
Mohamed S. Ibrahim;Ahmed Mohamed
Affiliations:
Knowledge Based Systems and Robotics Department, Mubarak City for Scientific Research and Technology Applications, Egypt;Networks and Distributed Systems Department, Mubarak City for Scientific Research and Technology Applications, Egypt
Venue:
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Year:
2006

Citing 8
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Abstract

This paper is concerned with the evolution development of the fuzzy neural networks. The learning procedures in such networks consists of two main development phases. First the network is evolutionary constructed through the use of evolutionary strategies and evolutionary programming. The second phase of the design of the network is aimed at its refinement and optimize the objective parameters to adapt to the nonlinear changes of the input space. There are two main optimization mechanisms working concurrently : structural development and paramertric refinement mechnisms using the integrated approach based on evolutionary programming and the evolution strategies.The evolutionary-based leaning has been compared with the gradientbased learning. The proposed technique is applied for controlling nonlinear dynamics of a multilink robot system. Extensive simulations have been performed, aimed at investigating the adaptive capability, tracking performance and the convergence properties of the proposed evolutionary model with respect to variety of situations. The results are remarkable compared with the fuzzy and neural networks optimized using gradient-based learning.