典型文献
Identification of important factors influencing nonlinear counting systems
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Xinmin ZHANG;Jingbo WANG;Chihang WEI;Zhihuan SONG
作者机构：
State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China
文献出处：
引用格式：
[1]Xinmin ZHANG;Jingbo WANG;Chihang WEI;Zhihuan SONG-.Identification of important factors influencing nonlinear counting systems)[J].信息与电子工程前沿（英文）,2022(01):123-133
A类：
GGPR
B类：
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AB值：
0.523776
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