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›› 2005, Vol. 22 ›› Issue (5): 631-638.DOI: 10.7523/j.issn.2095-6134.2005.5.013

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Reinforcement-Learning-Based On-Line Neural-Fuzzy Control System

LI Jia-Ning, YI Jian-Qiang, ZHAO Dong-Bin, XI Guang-Cheng   

  1. Lab of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
  • Received:2004-10-20 Revised:2004-11-22 Online:2005-09-15

Abstract:

To solve non-training data based on-line learning for neural-fuzzy controller, this paper proposes a reinforcement learning-based neural-fuzzy control system and the corresponding learning algorithm. This control system consists of a neural-fuzzy predictor and a neural-fuzzy controller. Based on fuzzy if then rules with certainty grades,the extended neural-fuzzy network proposed is used as the structure of the neural-fuzzy controller. In learning process of the control system, by using reinforcement signal to estimate the desired output of an input state, reinforcement learning is treated and solved according to training data-based learning algorithm. Computer simulations illustrate the rationality and applicability of the proposed control system and the learning algorithm.

Key words: neural2fuzzy predictor, neural2fuzzy controller, reinforcement learning, fuzzy rule, certainty grade

CLC Number: