非参数统计
非线性系统
泊松分布
核(代数)
非线性系统辨识
鉴定(生物学)
计算机科学
泊松回归
计数过程
系统标识
高斯分布
核回归
过程(计算)
机器学习
灵敏度(控制系统)
人工智能
数学
数据挖掘
统计
工程类
人口学
人口
度量(数据仓库)
社会学
物理
组合数学
操作系统
生物
量子力学
植物
电子工程
作者
Xinmin Zhang,Jingbo Wang,Chihang Wei,Zhihuan Song
标识
DOI:10.1631/fitee.2000324
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.
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