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

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.

Automated summary

A consistency verification method based on fuzzy c-means and spectral feature extraction is proposed to enhance the consistency verification ability of data transmission in Petri net. The method has a false alarm rate of 30% and a reconstruction accuracy above 80%, showing strong channel balance and adaptability to improve data output quality and accuracy comparison of redundant data reorganization in Petri net.