北京航空航天大学学报 ›› 2015, Vol. 41 ›› Issue (1): 96-101.doi: 10.13700/j.bh.1001-5965.2014.0076

• 论文 • 上一篇    下一篇

基于约束边长FART-Q的智能决策算法

周亚楠, 龚光红   

  1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100191
  • 收稿日期:2014-02-27 出版日期:2015-01-20 发布日期:2015-02-04
  • 通讯作者: 龚光红(1968-),女,四川石柱人,教授,ggh@buaa.edu.cn,主要研究方向为分布仿真与虚拟技术. E-mail:ggh@buaa.edu.cn
  • 作者简介:周亚楠(1990-),男,安徽泗县人,博士生,zyn_asee@126.com

Intelligent decision-making algorithm based on bounded FART-Q

ZHOU Yanan, GONG Guanghong   

  1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2014-02-27 Online:2015-01-20 Published:2015-02-04

摘要:

针对模糊自适应共振理论(ART)应用于智能决策时存在的问题,提出了约束边长的模糊ART算法.将有边长约束的模糊ART与Q学习结合,构建了约束边长FART-Q(Fuzzy ART-Q learning)智能决策网络.传统的模糊ART只根据输入向量与权值向量的模糊相似度进行分类,在用于智能决策中的状态分类时,不能考虑状态变量的物理含义,存在分类不合理的问题.针对这一问题,提出了对模糊ART的共振条件加入边长约束的改进算法,使得分类时可根据状态变量的物理含义确定分类的边长约束,同时能够减少分类数量.雷区导航仿真实验表明,约束边长FART-Q能快速做出合理决策.改进的模糊ART算法能够使分类更为合理,既能提高决策的成功率,又可以减小决策的运算时间.

关键词: 人工神经网络, 自适应共振理论, 模糊集理论, Q学习, 智能决策

Abstract:

Fuzzy adaptive resonance theory (ART) with bounded side length was proposed to address the problem emerged while applying fuzzy ART to intelligent decision-making. Integrating the modified fuzzy ART and Q learning algorithm, bounded fuzzy ART-Q learning (FART-Q) intelligent decision-making network was built. The original fuzzy ART might make unreasonable classifications only according to the fuzzy similarity between input vector and weight vector, without considering the physical meaning of the state variables. To solve this problem, a modified algorithm was proposed, strengthening the resonance condition of fuzzy ART with bounded side length. The improvement made it possible both to limit the side length according to the physical meaning of the state variables and to reduce the number of categories. The minefield navigation simulation was conducted to verify the availability and effectiveness of bounded FART-Q. Compared with the original fuzzy ART, the modified algorithm is able to make classifications more reasonably with higher success rate and less operation time.

Key words: artificial neural network, adaptive resonance theory, fuzzy set theory, Q learning, intelligent decision-making

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