New Results on PWARX Model Identification Based on Clustering Approach-Reference-Cited by-同舟云学术

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New Results on PWARX Model Identification Based on Clustering Approach

Published:2014-04 Issue:2 Volume:11 Page:180-188
ISSN:1476-8186
Container-title:International Journal of Automation and Computing
language:en
Short-container-title:Int. J. Autom. Comput.

Author:

Lassoued Zeineb,Abderrahim Kamel

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11633-014-0779-4.pdf

Reference32 articles.

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2. X. Feng, K. A. Loparo, Y. Ji, H. J. Chizeck. Stochastic stability properties of jump linear systems. IEEE Transactions on Automatic Control, vol. 37, no. 1, pp. 38–53, 1992.

3. A. Doucet, N. J. Gordon, V. Krishnamurthy. Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing, vol. 49, no. 3, pp. 613–624, 2001.

4. A. Bemporad, M. Morari. Control of systems integrating logic, dynamics, and constraints. Automatica, vol. 35, pp. 407–428, 1999.

5. A. Bemporad, G. Ferrari-Trecate, M. Morari. Observability and controllability of piecewise affine and hybrid systems. IEEE Transactions on Automatic Control, vol. 45, no. 10, pp. 1864–1876, 2000.

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