模糊Petri网模型的反向推理算法

Reverse Reasoning Algorithm Based on the Fuzzy Petri Net Model

提出了一种建立在模糊Petri网的基本结构上的反向推理算法 .通过建立模糊Petri网模型的关联矩阵、库所向量和变迁向量 ,运用矩阵运算的基本方法实现 .通过该算法的运行 ,可以在模糊Petri网模型中抽取出一个子模型 ,从而把一个大的、复杂的系统转化为一个只与问题相关的小的系统来处理 .采用数学运算的方法实现的反向推理算法简单 ,具有通用性 ,它适用于各种类型的模糊Petri网结构 .对于其它的大系统生成子系统的问题 ,这种矩阵运算的方法也可以借鉴 .同时对该算法中的矩阵运算和模型中的图形结构之间的关系进行了分析 。

This paper proposes a reverse reasoning algorithm on the basic struture of Fuzzy Petri Net model. Based on the model, an incidience matrix,a palce vector and a transition vector are built, with the method of matrix operation used in the algorithm. A subnet from the FPN can be extracted by operating the algorithm. Therefore a large and complex system can be transformed into a small system relating to the problems. Being simple and universal, this algorithm can bc applied to the majority of FPN model. For other problems involving a large system producing sub -systems, the matrix expression method can be used for reference. The relation is analyzed between the matrix expression in the algorithm and the graphics stuctrue in the model, and its complexity is also discussed in this paper.