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Implementation of recurrent multi-models for system identification

Abstract : Multi-modeling is a recent tool proposed for modeling complex nonlinear systems by the use of a combination of relatively simple set of local models. Due to their simplicity, linear local models are mainly used in such structures. In this work, multi-models having polynomial local models are described and applied in system identification. Estimation of model's parameters is carried out using least squares algorithms which reduce considerably computation time as compared to iterative algorithms. The proposed methodology is applied to recurrent models implementation. NARMAX and NOE multi-models are implemented and compared to their corresponding neural network implementations. Obtained results show that the proposed recurrent multi-model architectures have many advantages over neural network models.
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Contributor : Rachid Malti <>
Submitted on : Thursday, October 25, 2007 - 5:04:43 PM
Last modification on : Tuesday, March 31, 2020 - 2:12:13 PM
Document(s) archivé(s) le : Monday, April 12, 2010 - 12:41:59 AM


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  • HAL Id : hal-00182394, version 1


Lamine Thiaw, Kurosh Madani, Rachid Malti, Gustave Sow. Implementation of recurrent multi-models for system identification. Fourth International Conference on Informatics in Control, Automation and Robotics, May 2007, Angers, France. pp.314-321. ⟨hal-00182394⟩



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