An auxiliary model based multi-innovation recursive least squares estimation algorithms for MIMO Hammerstein system

An auxiliary model based multi-innovation recursive least squares estimation algorithms is proposed in this paper. The unknown variables in the information vector can be estimated by using the auxiliary model. The proposed recursive least squares algorithm uses not only the current innovation but al...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings of the 30th Chinese Control Conference S. 1442 - 1445
Hauptverfasser: Wang Xiuping, Chen Jing
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2011
Schlagworte:
Adaptation models
Auxiliary model identification
Convergence
Least squares
Least squares approximation
Multi-innovation identification
Multi-input multi-output systems
Nonlinear systems
Parameter estimation
Stochastic processes
Technological innovation
ISBN:9781457706776, 1457706776
ISSN:1934-1768
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:An auxiliary model based multi-innovation recursive least squares estimation algorithms is proposed in this paper. The unknown variables in the information vector can be estimated by using the auxiliary model. The proposed recursive least squares algorithm uses not only the current innovation but also the past innovations at each recursion and thus the parameter estimation accuracy can be improved. Finally, the simulation results indicate that the proposed algorithm has good performances.
ISBN:9781457706776
1457706776
ISSN:1934-1768