【摘要】 模糊Petri网模型可以用于模糊推理,但在Petri网中不得不用独立子网表示所有类型的过程,即使这些过程具有相同的行为,这导致整个Petri网变得很大。文中提出基于模糊有色Petri网的不确定性推理方法和基于遗传算法的学习过程。在减少网络规模,计算时间和克服解释网络困难的同时,它能保持等量的信息,提供结构化的表示,使知识库中的规则之间的关系易于表示。更多还原

【Abstract】 Fuzzy Petri Net can be used to perform fuzzy reasoning automatically, but in the Petri Nets we have to represent all kinds of processes by separate subnets even though the process has the same behavior of other one. This means that the total Petri Nets becomes very large. This paper presents a reasoning method using Fuzzy Coloured Petri Nets (FCPN) under uncertainty and a learning process using Genetic Algorithm. Hence, it can keep equally amount of information, while it decrease its net size, computing time and difficulty on explanation of network, and it can provide a structured representation in which the relationship among the rules in the knowledge base is easily understood.更多还原