一种模糊Petri网的逆向知识推理方法设计实现  

Design and Implement on a Backward Reasoning Algorithm Based on Fuzzy Petri Net

作　　者：杨劲松[1] 凌培亮[1] 

机构地区：[1]同济大学机械工程学院,上海200092

出　　处：《计算机科学》2009年第12期158-160,共3页Computer Science

摘　　要：模糊Petri网是模糊产生式知识表示和推理的理想工具。针对基于模糊产生式规则的知识库,在已知决策目标的前提下,设计了该知识库的模糊Petri网模型及基于递归的逆向知识推理方法,并以实例对该方法进行了验证。对于任意指定的库所,通过该方法可以确定其模糊托肯值,即对应命题的模糊真值。该方法的逻辑表达力强,利于计算机实现,而且其逆向推理策略能有效减少计算空间,使计算在一个复杂的模糊Petri网系统的子系统中进行,提高了计算效率。Fuzzy Petri net is a ideal tool for fuzzy production knowledge representation and reasoning. This paper designed the FPN model for fuzzy production rule-based knowledge base and implemented a backward fuzzy reasoning algorithm based on FPN through the reeursive. The algorithm was verified by examples in this paper. Using the algo-rithm,one can calculate the tokens of any appointed places which correspond to the true fuzzy values of the relevant propositions. The algorithm is strong in logic expression and easy to implement in computer system. The proposed algorithm can effectively reduce the computing space through transforming a large and complex system based on FPN into a small subsystem relating to the problems and improve the computational efficiency.

关 键 词：FPN DSS 知识表示 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]