机械与电子2026，Vol.44Issue(8)：18-25,8.
基于脉冲神经网络的数字车辆数据处理研究
Research on Digital Vehicle Data Processing Based on Spiking Neural Network
摘要
Abstract
To address the high resource consumption and long response latency of multi-source real-time data processing in the digital vehicle integrated information management platform,a processing meth-od based on spiking neural networks is proposed.Real-time data from various sensors,including vehicle speed,position,and temperature,are first converted into spike trains through spike encoding.The Spike-Timing Dependent Plasticity(STDP)learning rule is then combined with a multi-layer network architec-ture to enable deep mining of temporal data.Experimental results demonstrate that the proposed model performs effectively in processing real-time data streams,achieving an accuracy of 92.5%and a response time of 35 ms,thereby effectively satisfying the demands of large-scale real-time data stream processing in railway transportation systems.关键词
铁路数字化管理/数字车辆/脉冲神经网络/实时数据流处理/脉冲编码Key words
railway digital management/digital vehicle/spiking neural networks/real-time data stream processing/spike coding分类
信息技术与安全科学引用本文复制引用
吴乐华,于东洋..基于脉冲神经网络的数字车辆数据处理研究[J].机械与电子,2026,44(8):18-25,8.基金项目
国铁集团科技创新项目(CRNCJ2024073) （CRNCJ2024073）
