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

doi:10.1631/FITEE.2000324

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非线性计数系统的关键因子辨识方法

常规文章 | Updated：2022-04-19

- 非线性计数系统的关键因子辨识方法
  Enhanced Publication
- Identification of important factors influencing nonlinear counting systems
- 信息与电子工程前沿（英文） 2022年23卷第1期页码：123-133
- Affiliations：
  
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou310027, China
- Author bio：
  
  ‡Corresponding author
  E-mail: wangjingbobo@zju.edu.cn;
  E-mail: chhwei@zju.edu.cn;
  E-mail: songzhihuan@zju.edu.cn
- Funds：
  
  National Natural Science Foundation of China(62003301;61833014);Natural Science Foundation of Zhejiang Province, China(LQ21F030018)
- DOI：10.1631/FITEE.2000324
  中图分类号： TP277
- 收稿：2020-07-06，
  
  录用：2021-01-06，
  
  纸质出版：2022-01-0
- Accepted：
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张新民, 王静波, 魏驰航, 等. 非线性计数系统的关键因子辨识方法[J]. 信息与电子工程前沿（英文）, 2022,23(1):123-133.

Xinmin ZHANG, Jingbo WANG, Chihang WEI, et al. Identification of important factors influencing nonlinear counting systems[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 123-133.
张新民, 王静波, 魏驰航, 等. 非线性计数系统的关键因子辨识方法[J]. 信息与电子工程前沿（英文）, 2022,23(1):123-133. DOI： 10.1631/FITEE.2000324.

Xinmin ZHANG, Jingbo WANG, Chihang WEI, et al. Identification of important factors influencing nonlinear counting systems[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 123-133. DOI： 10.1631/FITEE.2000324.

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。

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.

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