Kernel density estimation of three-parameter Weibull distribution with neural network and genetic algorithm

被引：15
|
作者：
Yang, Fan [1 ]
Yue, Zhufeng [1 ]
机构：
[1] Northwestern Polytech Univ, Dept Engn Mech, Xian 710072, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 247卷
基金：
中国国家自然科学基金;
关键词：
Neural network model; Genetic algorithm; Weibull distribution; Optimization algorithm; Maximum likelihood method; Grey model; REGRESSION-MODELS; PARAMETERS;
D O I：
10.1016/j.amc.2014.09.065
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Three-parameter Weibull distribution is widely employed as a model in reliability and lifetime studies due to its good fit to data. It is important to estimate the unknown parameters exactly for modeling. There are many methods to estimate the parameters of three-parameter Weibull distribution and the kernel density estimation method is one of them. The smoothing parameter has a significant influence on the estimation accuracy. In this paper, the neural network and genetic algorithm were used to get the best smoothing parameter and the result was compared with other methods. The Monte Carlo simulations were carried out to show the feasibility of our approach for estimation of three-parameter Weibull distribution. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码：803 / 814
页数：12
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