Electronic Communication Fault Signal Recognition Based on Data Mining Algorithm

• Published in: Wireless communications and mobile computing Vol. 2022; no. 1
• Main Author: Luo, Jun
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Back propagation networks; Cluster analysis; Clustering; Communications networks; Communications systems; Data analysis; Data mining; Decision making; Design; Euclidean geometry; Fault diagnosis; Humidity; Information processing; Information technology; Methods; Neural networks; Recognition; Sensors; Vector quantization
• ISSN: 1530-8669; 1530-8677; 1530-8677
• DOI: 10.1155/2022/9641328

In order to solve the technical problem of fault signal recognition in the field of communication, this paper proposes an electronic communication fault signal recognition method based on data mining algorithm. Firstly, the K-means clustering algorithm is used to determine the cluster number k according to some attributes or class characteristics of the communication class samples, and the communication sample types are classified into a certain class so that the communication sample data in the cluster can be closely distributed and the data within a certain class range can be calculated by Euclidean distance formula. Then, this paper clusters the data. In the clustering data, BP neural network model is used to train and calculate the obtained clustering data again, which can map and deal with the complex nonlinear relationship between the fault information data of different clustering categories. The results show that the final error accuracy can be raised to about 20% by using the method in this paper. Conclusion. The algorithm designed in this paper can quickly predict the factors affecting the communication and find the communication fault information.