摘要
为提高电子战无人机的作战能力,提出一种基于粒计算和改进极限学习机(ELM)的作战效能评估方法。在该方法中,通过统计分析构建了电子战无人机作战效能评估指标体系;利用粒计算的约简功能,从诸多指标中提取出关键因素,据此利用蝙蝠算法优化的ELM构建电子战无人机作战效能的评估模型。仿真表明:该方法简化了网络结构,提高了建模效率,改善了模型精度,克服了传统ELM方法的不足,从而为电子战无人机的作战效能评估提供了一种有效的解决手段。
In order to improve the combat capability of Electronic Warfare Unmanned Air Vehicle(EWUAV),a combat effectiveness evaluation method is proposed based on Granular Computing(GrC)and modified Extreme Learning Machine(ELM).In the method,the combat effectiveness evaluation index system of EWUAV is constructed through statistical analysis,then using the reduction function of GrC key factors are extracted from many indices,finally the model of EWUAV combat effectiveness evaluation is established through ELM optimized by bat algorithm.Simulation results show that,the method simplifies the network structure,increases the modeling efficiency,improves the model accuracy,which overcomes the shortcomings of traditional ELM method,and provides an effective solution for EWUAV combat effectiveness evaluation.
作者
魏燕明
甘旭升
李寰宇
孟祥伟
WEI Yanming;GAN Xusheng;LI Huanyu;MENG Xiangwei(Xijing University,Xi’an 710123,China;Air Traffic Control and Navigation College,Air Force Engineering University,Xi’an 710051,China)
出处
《火力与指挥控制》
CSCD
北大核心
2022年第9期98-103,共6页
Fire Control & Command Control
关键词
电子战无人机
作战效能评估
粒计算
蝙蝠算法
极限学习机
electronic warfare unmanned air vehicle
combat effectiveness evaluation
granulation computing
bat algorithm
extreme learning machine
