| 引用本文: | 吴耿锋,傅忠谦.基于增强型算法并能自动生成规则的模糊神经网络控制器[J].控制理论与应用,2001,18(2):241~244.[点击复制] |
| WU Geng-feng,FU Zhong-qian.Reinforcement Based Fuzzy Neural Network Control with Automatic Rule Generation[J].Control Theory and Technology,2001,18(2):241~244.[点击复制] |
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| 基于增强型算法并能自动生成规则的模糊神经网络控制器 |
| Reinforcement Based Fuzzy Neural Network Control with Automatic Rule Generation |
| 摘要点击 1527 全文点击 1575 投稿时间：1999-01-19 修订日期：2000-04-13 |
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| DOI编号 |
| 2001,18(2):241-244 |
| 中文关键词 增强型学习 规则生成 神经网络 模糊控制器 |
| 英文关键词 reinforcement learning rule generation neural network fuzzy controller |
| 基金项目 |
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| 中文摘要 |
| 给出了一种基于增强型算法并能自动生成控制规则的模糊神经网络控制器RBFNNC(reinforcements based fuzzy neural network controller). 该控制器能根据被控对象的状态通过增强型学习自动生成模糊控制规则. RBFNNC用于倒立摆小车平衡系统控制的仿真实验表明了该系统的结构及增强型学习算法是有效和成功的. |
| 英文摘要 |
| A reinforcement based fuzzy neural network controller(RBFNNC) is proposed. A set of optimised fuzzy control rules can be automatically generated through reinforcement learning based on the state variables of object system. RBFNNC was applied to a cart pole balancing system and shows significant improvements on the rule generation. |