Jingting Mei, Yang Yang*, Zhipeng Gao, Lanlan Rui, Yijing Lin
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4883-4904, 2024, DOI:10.32604/cmc.2024.051860
- 20 June 2024
Abstract Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management. Considering the unique characteristics of edge networks, such as limited resources, complex network faults, and the need for high real-time performance, enhancing and optimizing existing network fault diagnosis methods is necessary. Therefore, this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network (LSNN). Firstly, we use the Izhikevich neurons model to replace the Leaky Integrate and Fire (LIF) neurons model in the LSNN model. Izhikevich… More >
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