The state space explosion, a challenge analogous to that encountered in a Petri net (PN), has constrained the extensive study of fuzzy Petri nets (FPNs). Current reasoning algorithms employing FPNs, which operate through forward, backward, and bidirectional mechanisms, are examined. These algorithms streamline the inference process by eliminating irrelevant components of the FPN. However, as the scale of the FPN grows, the complexity of these algorithms escalates sharply, posing a significant challenge for practical applications. To address the state explosion issue, this work introduces a parallel bidirectional reasoning algorithm for an FPN that utilizes reverse and decomposition strategies to optimize the implementation process. The algorithm involves hierarchically dividing a large-scale FPN into two sub-FPNs, followed by a converse operation to generate the reversal sub-FPN for the right-sub-FPN. The detailed mapping between the original and reversed FPNs is thoroughly discussed. Parallel reasoning operations are then conducted on the left-sub-FPN and the resulting reversal right-sub-FPN, with the final result derived by computing the Euclidean distance between the outcomes from the output places of the two sub-FPNs. A case study is presented to illustrate the implementation process, demonstrating the algorithm's significant enhancement of inference efficiency and substantial reduction in execution time.

基于可逆和动态分解机制的层次化FPN并行故障诊断

与Petri网类似,模糊Petri网(fuzzy Petri net, FPN)的研究同样受到状态空间爆炸问题的限制。目前,基于FPN的推理算法主要依赖于正向、反向和双向等机制。这些算法通过消除FPN中不相关的部分来简化推理过程。然而,随着规模的扩大,基于FPN的相关应用算法的复杂度迅速增加,这给基于FPN的推理算法的实际应用带来重大挑战。为解决状态爆炸问题,本文提出一种基于可逆和动态分解机制的FPN双向推理算法,以优化推理过程。该算法将层次化后的FPN分解为左右两个子网;然后,深入分析FPN原网与其逆网元素之间的对应关系,提出FPN逆网生成算法,用于生成右子网的逆网;最后,在左子网与右子网的逆网上同时执行推理算法,通过计算两子网输出位置之间的欧式距离得到最终结果。案例表明,本文提出的推理算法显著提高了推理效率,大幅缩短了执行时间。