摘要
针对经典洗出算法参数选择不当导致信号缺失,引起失真,影响洗出效果等问题,提出一种改进的多目标人工蜂群算法,利用该算法对经典洗出算法中的滤波器参数进行优化来改善洗出算法的洗出效果。针对传统蜂群算法初始化和局部优化中存在的问题,引入Circle映射和Pareto局部优化算法;建立人体感知误差模型、加速度差值模型、位移模型,将模型函数作为目标函数,用改进后的多目标人工蜂群算法对经典洗出算法进行参数优化;建立仿真模型对优化后的洗出算法进行仿真验证,应用飞行模拟器运动实验平台进行实验验证。结果表明:经优化后的洗出算法,洗出逼真度得到有效提升,降低了误差峰值,改善了相位延迟,节省了运动空间。
Addressing the issues such as signal loss,distraction,and bad wash-out effect caused by improper parameter selection in classic wash-out algorithms,an improved multi-objective artificial bee colony algorithm is proposed to optimize the filter parameters of the classical wash-out algorithm to improve the effect.For the problems in the initialization and local optimization of traditional swarm algorithm,Circle mapping and Pareto local optimization algorithm are introduced.The human perception error model,acceleration difference model,and displacement model are established,and the model function is used as the objective function,the parameters of the classical wash-out algorithm is optimized by the improved multi-objective artificial bee colony algorithm.A simulation model is established to simulate and verify the optimized wash-out algorithm,and a flight simulator motion test platform is applied to test and verify the algorithm.The results show that,with the optimized washout algorithm,the washout fidelity is effectively improved,the error peak is reduced,the phase delay is improved,and the motion space is saved.
作者
王辉
彭乐
Wang Hui;Peng Le(College of Aeronautical Engineering,Civil Aviation University of China,Tianjin 300300,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2024年第2期436-448,共13页
Journal of System Simulation
基金
国家自然科学基金委员会与中国民用航空局联合资助(U1733128)。
关键词
多目标优化
人工蜂群算法
洗出算法
参数优化
动感逼真度
multi-objective optimization
artificial bee colony algorithm
wash-out algorithm
parameter optimization
dynamic fidelity
