Self-adaptative multi-kernel algorithm for switched linear systems identification
by Lamaa Sellami; Salah Zidi; Kamel Abderrahim
International Journal of Modelling, Identification and Control (IJMIC), Vol. 31, No. 1, 2019

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.

Online publication date: Tue, 11-Dec-2018

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