基于直觉模糊Petri网的知识表示和推理  

Knowledge Representation and Reasoning Using Intuitionistic Fuzzy Petri Nets

作　　者：孟飞翔[1] 雷英杰[1] 余晓东[1] 雷阳[2] 

机构地区：[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

出　　处：《电子学报》2016年第1期77-86,共10页Acta Electronica Sinica

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

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.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.

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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]