Simulation of Web Page Big Data Capture Method Based on WNN Optimized by Locust Algorithm

Authors

  • Zhao-yin Jiang School of Information Engineering, Yangzhou Polytechnic College, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2172

Keywords:

Wavelet neural network, locust optimization algorithm, web page big data capture

Abstract

In order to improve web page big data capturing ratio, the wavelet neural network (WNN) optimized by improved locust algorithm is established for capturing web page big data. First, the web page big data method is established, and the corresponding mathematical model is studied. Secondly, the neural network is established, and Legendre wavelet basis function is used as excitation function of hidden layer of WNN, and the theory models of input layer, output layer and hidden layer are constructed, and then the improved locust optimization algorithm is designed based on Levy flight local search strategy, linear decreasing parameter random jump strategy, decreasing coefficient update strategy and weight coefficient update strategy. Finally, a case study is carried out for validating the proposed web big data capturing method, results illustrate that the proposed method based on WNN optimized by improved locust algorithm can effectively improve web page big data capturing efficiency and accuracy, which has wide application view.

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Author Biography

Zhao-yin Jiang, School of Information Engineering, Yangzhou Polytechnic College, China

Zhao-yin Jiang received his Bachelor Degree in Computational Mathematics and Applied Software from Shandong Institute of Mining and Tecnology, China; and Master Degree in Computer Technology from Jiangsu University of Science and Technology. Currently he teaches in Yangzhou Polytechnic College and and presides over two provincial teaching projects. His research area mainly focuses on big data technology and data mining.

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Published

2022-11-25

Issue

Section

Advanced Practice in Web Engineering