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On-line Access: 2022-01-24

Received: 2020-07-06

Revision Accepted: 2022-04-22

Crosschecked: 2021-01-06

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xinmin ZHANG

https://orcid.org/0000-0002-4761-3969

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.1 P.123-133

http://doi.org/10.1631/FITEE.2000324


Identification of important factors influencing nonlinear counting systems


Author(s):  Xinmin ZHANG, Jingbo WANG, Chihang WEI, Zhihuan SONG

Affiliation(s):  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   xinminzhang@zju.edu.cn, wangjingbobo@zju.edu.cn, chhwei@zju.edu.cn, songzhihuan@zju.edu.cn

Key Words:  Important factors, Nonlinear counting system, Generalized Gaussian process regression, Sensitivity analysis, Steel casting-rolling process


Xinmin ZHANG, Jingbo WANG, Chihang WEI, Zhihuan SONG. Identification of important factors influencing nonlinear counting systems[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 123-133.

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author="Xinmin ZHANG, Jingbo WANG, Chihang WEI, Zhihuan SONG",
journal="Frontiers of Information Technology & Electronic Engineering",
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pages="123-133",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000324"
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Abstract: 
Identifying factors that exert more influence on system output from data is one of the most challenging tasks in science and engineering. In this work, a sensitivity analysis of the generalized Gaussian process regression (SA-GGPR) model is proposed to identify important factors of the nonlinear counting system. In SA-GGPR, the GGPR model with Poisson likelihood is adopted to describe the nonlinear counting system. The GGPR model with Poisson likelihood inherits the merits of nonparametric kernel learning and Poisson distribution, and can handle complex nonlinear counting systems. Nevertheless, understanding the relationships between model inputs and output in the GGPR model with Poisson likelihood is not readily accessible due to its nonparametric and kernel structure. SA-GGPR addresses this issue by providing a quantitative assessment of how different inputs affect the system output. The application results on a simulated nonlinear counting system and a real steel casting-rolling process have demonstrated that the proposed SA-GGPR method outperforms several state-of-the-art methods in identification accuracy.

非线性计数系统的关键因子辨识方法

张新民,王静波,魏驰航,宋执环
浙江大学控制科学与工程学院工业控制技术国家重点实验室,中国杭州市,310027
摘要:从数据中识别对系统输出产生较大影响的关键因子是科学和工程领域最具挑战性的任务之一。本文针对非线性计数系统,提出基于敏感性分析的广义高斯过程回归(SA-GGPR)建模方法,以识别影响系统输出的关键因子。SA-GGPR采用具有泊松似然的GGPR模型描述非线性计数系统。GGPR模型继承了非参数核学习和泊松分布的优点,可处理复杂非线性计数系统。然而,由于GGPR模型的非参数核学习架构,难以理解GGPR模型中输入和输出之间的关系。SA-GGPR方法通过定量评估不同输入对系统输出的影响来辨识影响系统输出的关键因子。在模拟非线性计数系统和实际钢铁轧制过程的应用结果表明,SA-GGPR方法在识别精度方面优于几种先进方法。

关键词:关键因子;非线性计数系统;广义高斯过程回归;敏感性分析;钢铁轧制过程

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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