Online Updating With a Probability-Based Prediction Model Using Expectation Maximization Algorithm for Reliability Forecasting

被引:13
|
作者
Hu, Chang-Hua [1 ]
Si, Xiao-Sheng [1 ,2 ]
Yang, Jian-Bo [3 ]
Zhou, Zhi-Jie [1 ]
机构
[1] Xian Inst Hitech, Xian 710025, Peoples R China
[2] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
[3] Univ Manchester, Manchester Business Sch, Manchester M15 6PB, Lancs, England
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2011年 / 41卷 / 06期
基金
中国国家自然科学基金;
关键词
Decision analysis; expectation maximization (EM); forecasting; recursive algorithms; uncertainty; EVIDENTIAL REASONING APPROACH; TIME-SERIES; DECISION-ANALYSIS; INCOMPLETE DATA; SYSTEMS; UNCERTAINTY; MAINTENANCE; RULE; OPTIMIZATION; REGRESSION;
D O I
10.1109/TSMCA.2011.2147304
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Recently, a novel prediction model based on the evidential reasoning (ER) approach is developed to forecast reliability in engineering systems. In order to determine the parameters of the ER-based prediction model, some optimization models have been proposed to train the ER-based prediction model. However, these models are implemented in an offline fashion and thus it is very expensive to train and retrain them when new information is available. This correspondence paper is concerned with developing the recursive algorithms for updating the ER-based prediction model from the probability-based point of view. Using the recursive expectation maximization algorithm, two recursive algorithms are proposed for updating the parameters of the ER-based prediction model under judgmental and numerical outputs, respectively. As such, the proposed algorithms can be used to fine tune the ER-based prediction model online once new information becomes available. We verify the proposed method via a realistic example with missile reliability data.
引用
收藏
页码:1268 / 1277
页数:10
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