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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

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, 1998, -1(-1): .

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journal="Frontiers of Information Technology & Electronic Engineering",
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year="1998",
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 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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