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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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Xinmin ZHANG


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


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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%T Identification of important factors influencing nonlinear counting systems
%A Xinmin ZHANG
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%A Chihang WEI
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%DOI 10.1631/FITEE.2000324

T1 - Identification of important factors influencing nonlinear counting systems
A1 - Xinmin ZHANG
A1 - Jingbo WANG
A1 - Chihang WEI
A1 - Zhihuan SONG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
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SP - 123
EP - 133
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000324

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.




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