Title: Multiple model approach for nonlinear system identification with mixed-Gaussian weighting functions

Authors: Lei Chen; Yongsheng Ding

Addresses: Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University, Shanghai 201620, China ' Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University, Shanghai 201620, China

Abstract: Multiple modelling strategies have been developed for industrial processes with multiple operating conditions. However, in most of the existing works, the adjacent operating points are not changed significantly. In this paper, a multiple model approach with the mixed-Gaussian weighting functions is proposed, and the mixed-Gaussian weighting functions as the probability distributions are assigned to each local model. Three different mixture weights are introduced: the mixture weights are pre-determined; the mixture weights are unknown matrix; and the mixture weights follow Gaussian distribution. Under the framework of the expectation-maximisation (EM) algorithm, the parameters of local models as well as those of the mixed-Gaussian weighting functions are estimated simultaneously. To illustrate the effectiveness of the proposed approach, a numerical example and a distillation column example are considered. Furthermore, an experimental study on a pilot-scale hybrid tank system is also provided to highlight the practical utility.

Keywords: mixed-Gaussian weighting functions; nonlinear process identification; multiple models; expectation-maximisation algorithm.

DOI: 10.1504/IJMIC.2017.087056

International Journal of Modelling, Identification and Control, 2017 Vol.28 No.4, pp.295 - 306

Available online: 23 Aug 2017 *

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