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Title: An approach to tune fuzzy controllers based on reinforcement learning
Authors: Dai, X
Li, CK
Rad, AB
Keywords: Fuzzy control
Fuzzy systems
Gradient methods
Inference mechanisms
Learning (artificial intelligence)
Issue Date: 2003
Publisher: IEEE
Source: The 12th IEEE International Conference on Fuzzy Systems, 2003 : FUZZ '03, 25-28 May 2003, v. 1, p. 517-522 How to cite?
Abstract: This paper proposes a new approach for the tuning of fuzzy controllers parameters based on reinforcement learning. The architecture of the proposed approach comprises of a Q estimator network (QEN) and a Takagi-Sugeno type fuzzy inference system (FIS). Unlike the most of the existing fuzzy Q-learning approaches, which select an optimal action based on finite discrete actions, while the proposed controller obtain the control output directly. With the proposed architecture, the learning algorithms for all the parameters of the Q estimator network and the FIS are developed based on the temporal difference methods as well as the gradient descent algorithm. The performance of the proposed design technique is illustrated by simulation studies of a vehicle longitudinal control system.
ISBN: 0-7803-7810-5
DOI: 10.1109/FUZZ.2003.1209417
Appears in Collections:Conference Paper

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