Authors:
Lamine Thiaw
1
;
Kurosh Madani
1
;
Rachid Malti
2
and
Gustave Sow
3
Affiliations:
1
Laboratoire Image, Signal et Systmes Intelligents (LISSI / EA 3956) IUT de Snart, Universit Paris XII, France
;
2
Laboratoire Automatique Productique et Signal University Bordeaux 1, France
;
3
LER, Ecole Suprieure Polytechnique de Dakar, Universit Cheikh Anta Diop, Senegal
Keyword(s):
System identification, non-linear systems, multi-model, recurrent models.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time Series and System Modeling
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.