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A self-learning fuzzy logic controller using genetic algorithms with reinforcements

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3 Author(s)
Chih-Knan Chiang ; Dept. of Electr. Eng., Nat. Central Univ., Chung-Li, Taiwan ; Hung-Yuan Chung ; Jin-Jye Lin

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:5 ,  Issue: 3 )