An l2-stable feedback structure for nonlinear adaptive filtering and identification

This paper proposes a feedback structure for the design of l 2-stable algorithms for nonlinear adaptive filtering and identification, and establishes explicit connections between classical schemes in IIR modeling and more recent results in H ∞ theory. In particular, two algorithms due to Feintuch an...

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Veröffentlicht in:	Automatica (Oxford) Jg. 33; H. 1; S. 13 - 30
Hauptverfasser:	Sayed, Ali H., Rupp, Markus
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 1997 Elsevier
Schlagworte:	Applied sciences averaging analysis bootstrap method Computer science; control theory; systems Control theory. Systems Exact sciences and technology feedback structure Gauss-Newton filters H∞ filters IIR modeling l2 stability loop transformation mean-value theorem Modelling and identification positive-real function pseudo-linear regression filter Small-gain theorem stochastic gradient filters averaging analysis pseudo-linear regression filter loop transformation Small-gain theorem IIR modeling l 2 stability positive-real function feedback structure stochastic gradient filters Gauss-Newton filters bootstrap method H ∞ filters mean-value theorem Averaging method Stability Adaptive algorithm Feedback system Gauss Newton method Adaptive filtering Bootstrap Real positive function System identification Mean value Non linear system
ISSN:	0005-1098, 1873-2836
Online-Zugang:	Volltext
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