Identifying factors that exert more influence on system output from data is one of the most challenging tasks in science and *** this work,a sensitivity analysis of the generalized gaussian process regression(SA-GGPR)...
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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 *** this work,a sensitivity analysis of the generalized gaussian process regression(SA-GGPR)model is proposed to identify important factors of the nonlinear counting *** SA-GGPR,the GGPR model with Poisson likelihood is adopted to describe the nonlinear counting *** GGPR model with Poisson likelihood inherits the merits of nonparametric kernel learning and Poisson distribution,and can handle complex nonlinear counting ***,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 ***-GGPR addresses this issue by providing a quantitative assessment of how different inputs affect the system *** 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.
In machine learning, gaussianprocessregression (GPR) has been gaining popularity due to its nonparametric Bayesian form. However, the traditional GPR model is designed for continuous real-valued outputs with a Gauss...
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ISBN:
(纸本)9788993215182
In machine learning, gaussianprocessregression (GPR) has been gaining popularity due to its nonparametric Bayesian form. However, the traditional GPR model is designed for continuous real-valued outputs with a gaussian assumption, which does not hold in some engineering application studies. For example, when the output variable is count data, it violates the assumptions of the GPR model. generalized gaussian process regression (GGPR) can overcome the drawbacks of the conventional GPR, and it allows the model outputs to be any member of the exponential family of distributions. Thus, GGPR is more flexible than GPR. However, since GGPR is a nonlinear kernel-based method, it is not readily accessible to understand the effect of each input variable on the model output. To tackle this issue, the sensitivity analysis of GGPR (SA-GGPR) is proposed in this work. SA-GGPR aims to identify factors that exert higher influence on the model output by utilizing the information from the partial derivative of the GGPR model output with respect to its inputs. The proposed method was applied to a nonlinear count data system. The application results demonstrated that the proposed SA-GGPR is superior to the PLS-Beta, PLS-VIP, and SA-GPR methods in identification accuracy.
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