Early Detection of Network Fault Using Improved Gray Wolf Optimization and Wavelet Neural Network

Author:

Pan Chengsheng1ORCID,Jin Aixin1ORCID,Yang Wensheng1ORCID,Zhang Yanyan1ORCID

Affiliation:

1. School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract

To address the problem of diagnostic accuracy and stability degradation caused by random selection of the initial parameters for the wavelet neural network (WNN) fault diagnosis model, this paper proposes a network troubleshooting model based on the improved gray wolf algorithm (IGWO) and the wavelet neural network. First, the convergence factor and policy for the weight update are redesigned in the IGWO algorithm. This study uses a nonlinear convergence factor to balance the global and local search capabilities of the algorithm and dynamically adjusts the weights according to the adaptability of the head wolf α to strengthen its leadership position. Thereafter, the initial weights and biases of the WNN are optimized using the IGWO algorithm. During the backpropagation of the WNN error, momentum factors are introduced to prevent the model from falling into local optimization. Experimental results show that the IGWO algorithm is far better than GWO in terms of convergence speed and convergence accuracy. Furthermore, the average diagnostic accuracy of the IGWO-WNN model on the KDD-CUP99 dataset reaches 99.22%, which is 1.15% higher than that of the WNN model, and the stability of the diagnostic results is significantly improved.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Monkeypox diagnosis based on Dynamic Recursive Gray wolf (DRGW) optimization;Biomedical Signal Processing and Control;2024-01

2. Disease Diagnosis Based on Improved Gray Wolf Optimization (IGWO) and Ensemble Classification;Annals of Biomedical Engineering;2023-07-14

3. Method of aeroengine fault diagnosis based on a Fingerprint map;Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering;2023-02-20

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