Minimum Initial Marking Estimation in Labeled Petri Nets Using Minimum Token Number Prediction

• 成果类型: Article
• 署名作者: Yue, Hao; Xu, Yakun; Hu, Hesuan; Wu, Weimin; Li, Lingxi
• 署名单位: China University of Petroleum; Nanyang Technological University; Xidian University; Zhejiang University; Zhejiang University; Purdue University System; Purdue University; Purdue University in Indianapolis
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3510521
• 发表日期: 2025
• 页码: 3296-3302
• 关键词: estimation; vectors; Petri nets; Firing; Prediction algorithms; Analytical models; Upper bound; SURVEILLANCE; labeling; computational efficiency; discrete event systems (DESs); initial marking estimation; label sequence (LS); Petri nets (PNs)

摘要

This article proposes an approach to addressing the problem of minimum initial marking (MuIM) estimation for labeled Petri nets (LPNs). We introduce the important concept of a label synthesis net for LPNs and develop a method for predicting the minimum number of tokens. By using this prediction method, we develop an algorithm that has polynomial complexity in the length of the observed label sequence for estimating MuIMs. An illustrative example is provided to show the effectiveness and efficiency of our proposed approach. Moreover, experimental results demonstrate its advantage over existing work. Finally, we provide a comparison of some representative studies in the literature for MuIM estimation.