收稿日期: 2004-10-20
修回日期: 2004-11-22
网络出版日期: 2005-09-15
基金资助
国家973项目(2003CB517106);科技部国际科技合作重点项目(2004DFB02100)资助
Reinforcement-Learning-Based On-Line Neural-Fuzzy Control System
Received date: 2004-10-20
Revised date: 2004-11-22
Online published: 2005-09-15
李佳宁 , 易建强 , 赵冬斌 , 西广成 . 一种基于强化学习的在线神经模糊控制系统[J]. 中国科学院大学学报, 2005 , 22(5) : 631 -638 . DOI: 10.7523/j.issn.2095-6134.2005.5.013
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.
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