火力与指挥控制Issue(9)：143-146,151,5.
基于蚁群神经网络的发射系统故障诊断
Research on Fault Diagnosis for Launch System Based on Ant Colony Neural Network
摘要
Abstract
Launch system is an important part of surface to air missile weapon system,and it is of great significance to research its fault diagnosis on improving the operational effectiveness of air defense missile system and rapid response capability. BP neural network is easy to fall into local minimum point and the convergence speed is slow in fault diagnosis. In order to overcome these shortcomings,this paper introduces ACO algorithm into BP neural network to optimize the thresholds and weights taking the plunger pump in the hydraulic system as an example. Therefore the probability of training algorithm to converge to global optima is improved. The experimental results show that ACO neural network have faster convergence speed,higher efficiency and recognition ability than BP neural network, and it effectively improves the accuracy and efficiency of fault diagnosis. It is a kind of effective and feasible method and has good application prospects.关键词
发射系统/蚁群算法/神经网络/柱塞泵/故障诊断Key words
launch system/ACO algorithm/neural network/plunger pump/fault diagnosis分类
军事科技引用本文复制引用
宋涛,舒涛,雷荣强,刘赞..基于蚁群神经网络的发射系统故障诊断[J].火力与指挥控制,2015,(9):143-146,151,5.基金项目
国家自然科学基金资助项目 （）
