火力与指挥控制2025，Vol.50Issue(1)：152-159,168,9.DOI:10.3969/j.issn.1002-0640.2025.01.021
基于PSO+SOM神经网络的无人机装备故障智能诊断研究
Research on Intelligent Diagnosis of UAV Equipment Failure Based on PSO+SOM Neural Network
摘要
Abstract
In view of the current problems of low manual fault diagnosis efficiency,less intelligent diagnosis methods,low fault identification accuracy rate of UAV equipment and slow convergence speed of SOM neural network,a fault intelligent diagnosis method based on PSO+SOM neural network is proposed.By improving PSO algorithm to optimize SOM neural network and comparing the improvement effects of SOM neural network with PSO,GA and ACO and comparing the fault diagnosis effects with LVQ,BP,traditional SOM and PSO+SOM neural network,the results show that the fault diagnosis model of PSO+SOM neural network has small moderate value,short discrimination time,less iterations,high accuracy and fast convergence speed,an efficient method for implementing the intelligent fault diagnosis of UAV equipment is provided.关键词
无人机/SOM神经网络/PSO算法/智能化/故障诊断Key words
UAV/SOM neural network/PSO algorithm/intelligent/fault diagnosis分类
通用工业技术引用本文复制引用
沈延安,陈强,杨克泉..基于PSO+SOM神经网络的无人机装备故障智能诊断研究[J].火力与指挥控制,2025,50(1):152-159,168,9.
