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Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input

Long Chen Linqing Wang Zhongyang Han Jun Zhao Wei Wang

自动化学报（英文版）2020，Vol.7Issue(5)：1437-1445,9.

自动化学报（英文版）2020，Vol.7Issue(5)：1437-1445,9.DOI:10.1109/JAS.2019.1911645

Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input

Long Chen ¹Linqing Wang ¹Zhongyang Han ¹Jun Zhao ¹Wei Wang¹

作者信息

1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
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摘要

关键词

Industrial time series/kernel dynamic Bayesian networks (KDBN)/prediction intervals (PIs)/variational inference

Key words

Industrial time series/kernel dynamic Bayesian networks (KDBN)/prediction intervals (PIs)/variational inference

引用本文复制引用

Long Chen,Linqing Wang,Zhongyang Han,Jun Zhao,Wei Wang..Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input[J].自动化学报（英文版）,2020,7(5):1437-1445,9.

基金项目

This work was supported by the National Key Research and Development Program of China (2017YFA0700300) and the National Natural Sciences Foundation of China (61533005, 61703071, 61603069). （2017YFA0700300）

自动化学报（英文版）

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ISSN：2329-9266

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