摘要
为了解决小样本条件下民机装备可靠性模型参数的估计问题,本文提出一种基于收缩系数的粒子群万有引力搜索算法(Contraction factor Particle Swarm Optimization-Gravitational Search Algorithm,CPSOGSA)的参数估计算法。该方法提升了粒子群算法寻优性能,有效提升威布尔模型的参数估计精度。通过算例证明:该方法可以很好地用于民机设备的小样本可靠性参数估计,估计结果具有较高的精度,且耗时更短,表明了该方法的有效性和可行性。
In order to solve the problem of estimating the parameters of the reliability model of civil aircraft equipment under small sample conditions,this paper proposes a parameter estimation algorithm based on the Contraction factor Particle Swarm Optimization-Gravitational Search Algorithm(CPSOGSA).The method improves the performance of the particle swarm algorithm for finding the best performance and effectively improves the parameter estimation accuracy of the Weibull model.It is proved that the method can be well used for small-sample reliability parameter estimation of civil aircraft equipment,and the estimation results have higher accuracy and shorter time consumption,which shows the effectiveness and feasibility of the method.
作者
陈国庆
郑波
黄健豪
Chen Guoqing;Zheng Bo;Huang Jianhao(Civil Aviation Flight University of China,Guanghan,618307,Sichuan,China)
出处
《中国民航飞行学院学报》
2023年第3期5-9,共5页
Journal of Civil Aviation Flight University of China
基金
四川省科技计划应用基础研究项目(2021YJ0591)
关键词
小样本
粒子群算法
万有引力搜索算法
可靠性评估
Small samples
Particle swarm algorithm
Gravitational search algorithm
Reliability estimation
