【摘 要】
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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 id
【机 构】
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State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhe
论文部分内容阅读
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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