A k-plane clustering algorithm for identification of hybrid systems

Authors:
M. Tabatabaei-Pour;K. Salahshoor;B. Moshiri
Affiliations:
Department of Automation and Instrumentation, Petroleum University of Technology, Tehran, Iran;Department of Automation and Instrumentation, Petroleum University of Technology, Tehran, Iran;CIPCE, School of Electrical and Computer Engineering, Universtiy of Tehran, Tehran, Iran
Venue:
ACMOS'06 Proceedings of the 8th WSEAS international conference on Automatic control, modeling & simulation
Year:
2006

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Abstract

A new algorithm for identification of discrete time Hybrid Systems in the Piece-Wise Affine (PWA) form is introduced. This problem involves the estimation of both the parameters of the affine submodels and the partition of the PWA map from data. At the first stage we propose a modified version of the k-plane clustering algorithm proposed in [1] to provide initial data classification and parameter estimation. Then we apply the refinement algorithm proposed in [11] repeatedly to the estimated clusters in order to improve both the data classification and the parameter estimation. The k-plane approach clusters the data in the data space instead of feature space and is computationally very efficient. Also the possible modifications on the algorithm which yield to a recursive version for online identification of PWA Hybrid systems are discussed.