Multi-innovation Extended Stochastic Gradient Algorithm and Its Performance Analysis

This paper derives the multi-innovation extended stochastic gradient algorithm for controlled autoregressive moving average models by expanding the scalar innovation to an innovation vector and analyzes its performance in detail. Four convergence theorems are given for the multi-innovation extended...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Circuits, systems, and signal processing Jg. 29; H. 4; S. 649 - 667
Hauptverfasser:	Liu, Yanjun, Yu, Li, Ding, Feng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston SP Birkhäuser Verlag Boston 01.08.2010 Springer Nature B.V
Schlagworte:	Algorithms Circuits and Systems Convergence Electrical Engineering Electronics and Microelectronics Engineering Estimates Instrumentation Mathematical analysis Mathematical models Parameter estimation Scalars Signal,Image and Speech Processing Stochastic models Stochasticity Theorems Vector space Vectors (mathematics) Signal processing Recursive identification Parameter estimation Performance analysis Multi-innovation identification Stochastic gradient
ISSN:	0278-081X, 1531-5878
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!