基于FA-RBF神经网络的导弹导引系统状态预测

李海君; 王文双; 赵建忠

doi:10.15892/j.cnki.djzdxb.2023.01.001

弹箭与制导学报 >

2023 , Vol. 43 >Issue 1: 1 - 7

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.01.001

基于FA-RBF神经网络的导弹导引系统状态预测

李海君 ,
王文双 ,
赵建忠

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海军航空大学,山东烟台 264001

李海君(1978—),男,工程师,博士,研究方向:装备保障、可靠性工程。

收稿日期: 2022-07-20

网络出版日期: 2025-02-01

基金资助

国家自然科学基金(51605487)

山东省自然科学基金(ZR2020QF057)

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State Prediction of Missile Guidance System Based on FA-RBF Neural Network

LI Haijun ,
WANG Wenshuang ,
ZHAO Jianzhong

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Naval Aviation University, Yantai 264001, Shandong, China

Received date: 2022-07-20

Online published: 2025-02-01

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摘要

导引系统是导弹组成部件中故障率相对较高的部分,对其状态进行预测和预防性维修是保持导弹完好率和保障作战效能的关键环节。导引系统具有内部组成复杂、测试指标繁多、系统状态难以确定等特点。为了快速准确地对导引系统进行状态预测,提出一种基于FA-RBF神经网络的状态预测方法。该方法根据自动测试设备给出的系统测试指标数据,采用因子分析(factor analysis, FA)方法降维处理测试指标数据,得到潜在关键因子,并计算因子得分。然后以因子得分为输入,反映系统状态的内部测点为输出,建立训练样本,运用径向基函数(radial basis function, RBF)神经网络进行导引系统状态预测。最后通过示例说明方法的实用性和有效性。

关键词： 导弹导引系统; 数据采集; 因子分析; 人工神经网络; RBF

本文引用格式

李海君 , 王文双 , 赵建忠 . 基于FA-RBF神经网络的导弹导引系统状态预测[J]. 弹箭与制导学报, 2023 , 43(1) : 1 -7 . DOI: 10.15892/j.cnki.djzdxb.2023.01.001

Abstract

Missile guidance system is a relatively high failure rate part of missile components. The prediction and preventive maintenance of its state are the key links to maintain the integrity rate of missiles and ensure operational efficiency. Missile guidance system has the characteristics of complex internal composition, many test indicators, and difficult to determine the system state. In order to predict the state of missile guidance system quickly and accurately, a state prediction method based on FA-RBF neural network is proposed. According to the system test index data given by the automatic test equipment, this method uses the factor analysis(FA) method to reduce the dimension of the test index data, obtain the potential key factors, and calculate the factor score. Then take factor score as the input and the internal measuring points that can reflect the system state as the output to establish the training samples. Radial basis function (RBF) neural network is used to predict the state of missile guidance system. Finally, an example is given to illustrate the practicality and effectiveness of the method.

Key words： missile guidance system; data collection; factor analysis; artificial neural network; radial basis function (RBF)

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