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Evolutionary fuzzy system for architecture control in a constructive neural network

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3 Author(s)
Calvo, R. ; Dept. of Comput. Sci. & Stat., Sao Paulo Univ., Brazil ; Figueiredo, M. ; Antonelo, E.A.

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi-Sugeno fuzzy rules and controls the architecture of a constructive neural network. The performance of the mobile robot guides the evolutionary learning mechanism. Experiments show the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture.

Published in:

Computational Intelligence in Robotics and Automation, 2005. CIRA 2005. Proceedings. 2005 IEEE International Symposium on

Date of Conference:

27-30 June 2005

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