Title: Self-adaptative multi-kernel algorithm for switched linear systems identification

Authors: Lamaa Sellami; Salah Zidi; Kamel Abderrahim

Addresses: Numerical Control of Industrial Processes, National Engineering School of Gabes, University of Gabes, Tunisia ' Department of Management Information, Systems College of Business and Economics, Qassim University, Saudi Arabia ' Numerical Control of Industrial Processes, National Engineering School of Gabes, University of Gabes, Tunisia

Abstract: This paper deals with the problem of switched linear system identification. This is one of the most difficult problems since it involves both the estimation of the linear sub-models and the switching instants. In fact, we propose an identification approach based on self-adaptation multi-kernel clustering algorithm to estimate simultaneously the linear sub-models and the switching signal. The estimation of the sub-models consists of decomposing the regression vector into several blocks and assigning a kernel function to each block. However, the estimation of the switching signal is provided by an unsupervised classification algorithm with self-adaptive capacities. Simulation results are presented to illustrate the effectiveness of the proposed approach.

Keywords: switched linear systems; system identification; multi-kernel support regression; machine learning.

DOI: 10.1504/IJMIC.2019.096792

International Journal of Modelling, Identification and Control, 2019 Vol.31 No.1, pp.103 - 111

Received: 04 Jan 2017
Accepted: 24 Mar 2017
Published online: 11 Dec 2018 *

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