电子学报 ›› 2016, Vol. 44 ›› Issue (1): 77-86.DOI: 10.3969/j.issn.0372-2112.2016.01.012

• 学术论文 • 上一篇    下一篇

基于直觉模糊Petri网的知识表示和推理

孟飞翔1, 雷英杰1, 余晓东1, 雷阳2   

  1. 1. 空军工程大学防空反导学院, 陕西西安 710051;
    2. 武警工程大学, 陕西西安 710086
  • 收稿日期:2014-07-15 修回日期:2015-02-11 出版日期:2016-01-25 发布日期:2016-01-25
  • 作者简介:孟飞翔 男,1986年11月出生,河南信阳人,现为空军工程大学计算机应用专业博士研究生,主要研究方向为智能信息处理. E-mail:ttimo@163.com 雷英杰 男,1956年11月出生,陕西华阴人,IEEE高级会员.现为空军工程大学教授,博士生导师,主要研究方向为智能信息处理与智能决策. E-mail:leiyjie@163.com
  • 基金资助:

    国家自然科学基金(No.61272011);国家自然科学青年基金(No.61309022)

Knowledge Representation and Reasoning Using Intuitionistic Fuzzy Petri Nets

MENG Fei-xiang1, LEI Ying-jie1, YU Xiao-dong1, LEI Yang2   

  1. 1. Air and Missile Defense College, Air Force Engineering University, Xi'an, Shaanxi 710051, China;
    2. Engineering University of Armed Police Force, Xi'an, Shaanxi 710086, China
  • Received:2014-07-15 Revised:2015-02-11 Online:2016-01-25 Published:2016-01-25

摘要:

针对模糊Petri网存在隶属度单一的问题,将直觉模糊集理论与Petri网理论相结合,构建直觉模糊Petri网(Intuitionistic Fuzzy Petri Nets,IFPN)模型,用于知识的表示和推理.首先构建了IFPN模型,并将其应用于知识的表示,通过在模型中引入抑止转移弧,解决了否命题的表示问题.其次提出了基于矩阵运算的IFPN推理算法,通过修改变迁触发后token值的传递规则,解决了推理过程中的事实的保留问题;通过修改变迁的触发规则,抑制了变迁的重复触发.最后对推理算法进行了分析,并举例验证了提出的IFPN模型及其推理算法的可行性,结果表明IFPN是对FPN的有效扩充和发展,其对推理结果的描述更加细腻、全面.

关键词: 直觉模糊Petri网, 直觉模糊产生式规则, 知识表示, 直觉模糊推理

Abstract:

Aimed at fuzzy Petri nets (FPN) existing membership single issue, intuitionistic fuzzy Petri nets (IFPN) was constructed for knowledge representation and reasoning by combining intuitionistic fuzzy sets theory and Petri net theory.Firstly, IFPN model was constructed for knowledge representation, and the issue of negative proposition representation was solved by introducing inhibitive transfer arcs into the model.Secondly, the algorithm based on matrix operation was presented, the issue of fact reservation in reasoning procedure was solved by modifying token value's transfer rules after transitions being fired, and the issue of transitions repeatedly being fired was inhibited by modifying firing rules of transitions.Lastly, the algorithm was analyzed, and the feasibility of proposed IFPN model and algorithm was proved through an example.The result indicates that IFPN is an effective extension and development of FPN, and it describes the reasoning result more delicately and comprehensively.

Key words: intuitionistic fuzzy Petri nets (IFPN), intuitionistic fuzzy production rules, knowledge representation, intuitionistic fuzzy reasoning

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