火力与指挥控制2025，Vol.50Issue(3)：130-134,141,6.DOI:10.3969/j.issn.1002-0640.2025.03.018
基于改进SSA-BP神经网络的弹丸全弹道飞行时间预测
Prediction of Full Ballistic Flight Time of Projectiles Based on Improved SSA-BP Neural Network
摘要
Abstract
The improved Sparrow Search Algorithm is used to globally optimize the weights and thresholds of BP neural network.In Sparrow Search Algorithm(SSA),Tent chaotic mapping strategy is in-troduced to initialize the population.At the same time,Gaussian mutation,Gaussian perturbation strategy and adaptive inertia weight strategy are introduced.The ability of SSA algorithm to jump out of the local optimization and its solving accuracy are improved,so as to improve the accuracy and stability of the pre-diction of the full ballistic flight time of the projectiles when the ballistic coefficient,firing Angle,wind speed and projectile muzzle velocity are known.The results show that the improved SSA-BP prediction model can improve the stability and accuracy of the full ballistic flight time prediction of projectiles.关键词
弹丸全弹道飞行时间/BP神经网络/SSA优化算法/Tent混沌映射/高斯变异Key words
full ballistic flight time of projectiles/BP neural network/SSA optimization algorithm/Tent chaotic mapping/gaussian mutation分类
军事科技引用本文复制引用
郝博,徐才宪,姜琦,杨斌..基于改进SSA-BP神经网络的弹丸全弹道飞行时间预测[J].火力与指挥控制,2025,50(3):130-134,141,6.基金项目
河北省自然科学基金(E2019501085) （E2019501085）
东北大学秦皇岛分校科研启动基金资助项目(XNY201808) （XNY201808）
