Multi-model multivariate Gaussian process modelling with correlated noises. (October 2017)

• Record Type: Journal Article
• Authors: Hong, Xiaodan; Huang, Biao; Ding, Yongsheng; Guo, Fan; Chen, Lei; Ren, Lihong
• Is Part Of: Journal of process control. Volume 58(2017)
• Page Start: 11
• Page End: 22
• Publication Date: 2017-10
• Subjects: Multivariate GPR -- Correlated Gaussian noises -- Gaussian mixture model (GMM) -- Multiple-model -- Dependent multivariate GPR (DMGPR) -- EM algorithm -- PSO algorithm; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.08.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Model estimation for multivariate, muliti-mode, and nonlinear processes with correlated noises. Mixture Gaussian model for estimation of model parameters under the Gaussian Process framework. The effectiveness is demonstrated by a three-level drawing process of Carbon fiber production. Abstract: A composite multiple-model approach based on multivariate Gaussian process regression (MGPR) with correlated noises is proposed in this paper. In complex industrial processes, observation noises of multiple response variables can be correlated with each other and process is nonlinear. In order to model the multivariate nonlinear processes with correlated noises, a dependent multivariate Gaussian process regression (DMGPR) model is developed in this paper. The covariance functions of this DMGPR model are formulated by considering the "between-data" correlation, the "between-output" correlation, and the correlation between noise variables. Further, owing to the complexity of nonlinear systems as well as possible multiple-mode operation of the industrial processes, to improve the performance of the proposed DMGPR model, this paper proposes a composite multiple-model DMGPR approach based on the Gaussian Mixture Model algorithm (GMM-DMGPR). The proposed modelling approach utilizes the weights of all the samples belonging to each sub-DMGPR model which are evaluated by utilizing the GMM algorithm when estimating model parameters through expectation and maximization (EM) algorithm. The effectiveness of the proposed GMM-DMGPR approach is demonstrated by two numerical examples and a three-level drawing process of Carbon fiber production.