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Construction of data transmission consistency verification model of Petri net based on fuzzy C-means

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Abstract

To enhance the consistency verification ability of data transmission in Petri net, a consistency verification method based on fuzzy c-means and spectral feature extraction was proposed. The invention adopts a fuzzy neural network clustering Petri net data transmission channel model design, test method of Petri nets jamming signal to extract the training data set of attributes in reduction and extraction to influence the decision attribute set classification of condition attribute values, form a Petri net jamming signal extraction and selection model, and to filter the interference of symbol sequence. The matching filter and fuzzy information clustering detection method are combined to realize the Petri net data transmission channel equalization. The false alarm rate of the proposed method is 30%, and the reconstruction accuracy is above 80%. The proposed method has strong channel balance and adaptability, which can improve the accuracy comparison of redundant data reorganization and enhance the data output quality of Petri net.

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Authors and Affiliations

  1. Department of Information Engineering, Yantai Vocational College, Yantai, 264670, China

    Zuopeng Wang

  2. Basic Teaching Department, Yantai Vocational College, Yantai, 264670, China

    Zhenying Zhu

Authors
  1. Zuopeng Wang
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  2. Zhenying Zhu
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Correspondence to Zuopeng Wang.

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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Wang, Z., Zhu, Z. Construction of data transmission consistency verification model of Petri net based on fuzzy C-means. Wireless Netw 28, 2313–2322 (2022). https://doi.org/10.1007/s11276-021-02747-3

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