火力与指挥控制2026，Vol.51Issue(3)：25-34,10.DOI:10.3969/j.issn.1002-0640.2026.03.004
一种基于ISSA-SVR融合模型的战时装备器材预测方法
A Wartime Equipment Spare Parts Forecasting Method Based on the ISSA-SVR Fusion Model
徐一鸣 1杜华 1刘银良1
作者信息
- 1. 北方自动控制技术研究所,太原 030006
- 折叠
摘要
Abstract
Accurate prediction of equipment spare parts is crucial to ensure military equipment availability and sustained combat capability.However,current wartime forecasting faces challenges including scarce historical samples,highly nonlinear patterns,and significant uncertainty.To address these challenges,this study proposes an ISSA-SVR fusion model with three key steps:First,constructing a damage indicator system quantified via fuzzy comprehensive evaluation.Second,augmenting limited datasets using Conditional Tabular Generative Adversarial Networks(CTGAN).Third,enhancing the Sparrow Search Algorithm(SSA)with cross-directional crossover and elite retention strategies.Experimental results demonstrate a maximum relative error of merely 3%,with prediction accuracy and stability that significantly outperforming outperforms methods.This approach provides effective technical support for wartime equipment spare parts forecasting.关键词
装备损伤/装备器材/消耗预测/麻雀搜索/支持向量回归/生成对抗网络Key words
equipment damage/spare parts/consumption forecasting/ISSA/SVR/CTGAN分类
军事科技引用本文复制引用
徐一鸣,杜华,刘银良..一种基于ISSA-SVR融合模型的战时装备器材预测方法[J].火力与指挥控制,2026,51(3):25-34,10.
