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基于约束边长FART-Q的智能决策算法

周亚楠 龚光红

周亚楠, 龚光红. 基于约束边长FART-Q的智能决策算法[J]. 北京航空航天大学学报, 2015, 41(1): 96-101. doi: 10.13700/j.bh.1001-5965.2014.0076
引用本文: 周亚楠, 龚光红. 基于约束边长FART-Q的智能决策算法[J]. 北京航空航天大学学报, 2015, 41(1): 96-101. doi: 10.13700/j.bh.1001-5965.2014.0076
ZHOU Yanan, GONG Guanghong. Intelligent decision-making algorithm based on bounded FART-Q[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(1): 96-101. doi: 10.13700/j.bh.1001-5965.2014.0076(in Chinese)
Citation: ZHOU Yanan, GONG Guanghong. Intelligent decision-making algorithm based on bounded FART-Q[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(1): 96-101. doi: 10.13700/j.bh.1001-5965.2014.0076(in Chinese)

基于约束边长FART-Q的智能决策算法

doi: 10.13700/j.bh.1001-5965.2014.0076
详细信息
    作者简介:

    周亚楠(1990-),男,安徽泗县人,博士生,zyn_asee@126.com

    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.

  • 中图分类号: TP183

Intelligent decision-making algorithm based on bounded FART-Q

  • 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.

     

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出版历程
  • 收稿日期:  2014-02-27
  • 网络出版日期:  2015-01-20

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