摘要
给出了一种改进的基于规则的逆向模糊推理算法。该算法基于产生式知识表示的模糊Petri网(FPN)模型,适用于一类基于规则的系统。利用该算法可以有效地对该类系统的FPN模型进行相应的处理。对于任意指定的库所,通过该算法可以确定其模糊托肯值,即对应命题的模糊真值。通过对具体的算例进行分析并与已有的算法进行比较后发现,该算法不仅可以得到同样精确的结果,而且该算法使FPN的推理过程更类似于人脑的逻辑推导过程,比较缜密。
An improved rule-based backward fuzzy reasoning algorithm was pointed. The algorithm is based on the fuzzy Petri net （FPN） which represents fuzzy production rules. The algorithm is also suit for a class of rule-based systems, i.e. it can deal with the fuzzy Petri net model which results from the real system. Via using the algorithm, one can calculate the tokens of any appointed places which correspond to the true fuzzy values of the relevant propositions. Through practical applications, the advantages of the proposed algorithm compared with those present results obtained by the existed algorithm.
出处
《通信学报》
EI
CSCD
北大核心
2008年第2期101-105,共5页
Journal on Communications
基金
国家重点基础研究发展计划(“973”计划)基金资助项目(2005CB321904)
上海市自然科学启明星计划项目(03QG14016)
上海市教委局管基金重点项目(04JG05063)~~
