使用门递归单元识别有界 Petri 网的合法转换序列

IF 7.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Big Data Pub Date : 2023-09-27 DOI:10.1109/TBDATA.2023.3319252

Qingtian Zeng;Shuai Guo;Rui Cao;Ziqi Zhao;Hua Duan

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引用次数: 0

摘要

门递归单元（GRU）在有界 Petri 网的合法转换序列应用方面有很大的空白。本文提出了一种基于 GRU 的有界 Petri 网合法转换序列识别方法。首先，在 Petri 网中，按照一定的噪声比生成合法过渡序列和非合法过渡序列。然后，将合法和非法过渡序列输入 GRU，通过将最大变化序列长度编码为统一长度来识别合法过渡序列。我们利用不同噪声比的 Petri 网对所提出的方法进行了验证，并与七种广为人知的基线方法进行了比较。结果表明，所提出的方法在大多数情况下都能达到出色的识别准确率和鲁棒性。解决了现有方法无法实时识别 Petri 网合法转换序列的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Legal Transition Sequence Recognition of a Bounded Petri Net Using a Gate Recurrent Unit

The Gate Recurrent Unit (GRU) has a large blank in the application of legal transition sequences for bounded Petri nets. A GRU-based method is proposed for the recognition of bounded Petri net legal transition sequences. First, in a Petri net, legal and non-legal transition sequences are generated according to a certain noise ratio. Then, the legal and non-legal transition sequences are inputted into GRU to recognize the legal transition sequences by encoding the maximum variation sequence length with a uniform length. The proposed method is validated with different Petri nets at different noise ratios and compared with seven widely-known baselines. The results show that the proposed method achieves excellent recognition accuracy and robustness in most situations. Solving the problem that the existing methods cannot recognize the legal transition sequences of Petri nets in real time.

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来源期刊

IEEE Transactions on Big Data Multiple-

CiteScore

11.80

自引率

2.80%

发文量

114

期刊介绍： The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.