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Published 1993 | Published
Book Section - Chapter Open

Learning Fuzzy Rule-Based Neural Networks for Control

Abstract

A three-step method for function approximation with a fuzzy system is proposed. First, the membership functions and an initial rule representation are learned; second, the rules are compressed as much as possible using information theory; and finally, a computational network is constructed to compute the function value. This system is applied to two control examples: learning the truck and trailer backer-upper control system, and learning a cruise control system for a radio-controlled model car.

Additional Information

© 1993 Morgan Kaufmann. This work was supported in part by Pacific Bell, and in part by DARPA and ONR under grant no. N00014-92-J-1860.

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Created:
August 20, 2023
Modified:
January 13, 2024