Network fault diagnosis method based on wavelet neural network, equipment and storage medium

Abstract

The invention discloses a network fault diagnosis method based on a wavelet neural network. The method comprises the following steps: S1, obtaining network data in a fault state and a normal state; S2, carrying out numeralization and normalization processing on the network fault data, and carrying out data dimension reduction by adopting a PCA dimension reduction algorithm; S3, creating a wavelet neural network model, selecting an improved grey wolf optimization algorithm, and taking optimized parameters as parameters of the wavelet neural network model; then taking the network fault data processed in the second step as input, adding momentum factors when parameters are reversely adjusted, and establishing a network fault diagnosis model through continuous training; S4, inputting real-time network state data, and judging whether the network has a fault; and S5, outputting a network fault diagnosis result and a fault category. The momentum factor is introduced, so that the local optimization capacity of the diagnosis model is improved; an improved grey wolf algorithm is adopted, initial parameters of a fault diagnosis model are optimized, and the randomness of initial parameter selection is avoided.

Description

technical field [0001] The invention relates to a network fault diagnosis method, equipment and storage medium, in particular to a network fault diagnosis method, equipment and storage medium based on wavelet neural network. Background technique [0002] During the operation of the network system, failure is a very worthy of attention. Even when a small failure occurs, it is very likely to affect the healthy operation of the entire network system, which may further lead to the paralysis of the entire system, causing economic and property damage. Loss. Therefore, it is of great significance to quickly and accurately discover various abnormalities in the network system through effective fault detection and diagnosis technology, locate faults, and restore faults to maintain the healthy operation of the network system. [0003] At present, fault diagnosis techniques are mainly divided into qualitative analysis methods and quantitative analysis methods. Among them, qualitative ...

AI Technical Summary

Problems solved by technology

However, the traditional wavelet neural network method is easy to fall into local optimum, which leads to the network fault diagnosis model not being optimal in terms of diagnosis accuracy.

In addition, the initial parameter selection of the WNN model is random, and the selection of different initial parameters may cause the model training to fall into different local optima, which will lead to fluctuations in the diagnosis results of network faults and poor diagnostic stability.

Images