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弹箭与制导学报 >

2014 , Vol. 34 >Issue 6: 43 - 46,50

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2014.06.011

导弹与制导技术

基于改进的LS-SVM 地空导弹生存能力的预测

  • 王改堂 ,
  • 王斐 ,
  • 黄超凡 ,
  • 郑松海 ,
  • 赵娜 ,
  • 叶锦函
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  • 1 中国兵器工业第203研究所,西安 710065
    2 河南科技大学信息工程学院,河南洛阳 471003

王改堂(1980-),男,陕西神木人,博士,研究方向:武器系统,弹载计算机,软测量。

收稿日期: 2014-01-06

  网络出版日期: 2025-05-30

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Prediction of Surface-to-air Missile Survivability Based on Improved Least Square Support

  • WANG Gaitang ,
  • WANG Fei ,
  • HUANG Chaofan ,
  • ZHENG Songhai ,
  • ZHAO Na ,
  • YE Jinhan
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  • 1 No. 203 Research Institute of China Ordnance Industries, Xi'an 710065, China
    2 School of Information Engineering, Henan University of Science and Technology, Henan Luoyang 471003, China

Received date: 2014-01-06

  Online published: 2025-05-30

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摘要

针对最小二乘支持向量机(LS-SVM)缺失的鲁棒性以及参数难以选择的问题,提出了改进的LS-SVM预测建模方法。该方法首先从样本特征重要程度的角度出发构造特征加权矩阵,然后利用该特征加权矩阵对LS-SVM进行加权,最后采用了人工鱼群优化算法选择模型参数。通过对基准数据集的仿真验证了该方法的有效性和可行性,并将其预测地空导弹生存能力,获得了满意的结果。

关键词： 最小二乘支持向量机; 特征加权; 人工鱼群算法; 地空导弹; 生存能力

本文引用格式

王改堂 , 王斐 , 黄超凡 , 郑松海 , 赵娜 , 叶锦函 . 基于改进的LS-SVM 地空导弹生存能力的预测[J]. 弹箭与制导学报, 2014 , 34(6) : 43 -46,50 . DOI: 10.15892/j.cnki.djzdxb.2014.06.011

Abstract

A novel prediction modeling algorithm based on improved LS-SVM was proposed to solve the problem of LS-SVM algorithm lacking robustness and hard parameter selection. Firstly, the method structures feature weighting matrix from the feature importance of sample, and then uses the matrix to weight for LS-SVM. Finally, the paper uses artificial fish swarm algorithm to choose parameters of the model. Simulation results verified the effectiveness and feasibility of this method by using some real-world datasets, and it was applied to develop a prediction model for surface-to-air missile survivability, and the result was satisfied.

Key words： least squares support vector machine (LS-SVM); feature weight; artificial fish swarm algorithm (AFSA); surface-to-airmissile; survivability

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