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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Vydané v:Automatica (Oxford) Ročník 33; číslo 1; s. 13 - 30
Hlavní autori: Sayed, Ali H., Rupp, Markus
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 1997
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Abstract 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 and to Landau, as well as the so-called pseudo-linear regression and Gauss-Newton algorithms, are discussed within the framework proposed here. Additional examples and simulation results are included to illustrate the applicability of the approach to several nonlinear scenarios.
AbstractList 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 and to Landau, as well as the so-called pseudo-linear regression and Gauss-Newton algorithms, are discussed within the framework proposed here. Additional examples and simulation results are included to illustrate the applicability of the approach to several nonlinear scenarios.
Author Rupp, Markus
Sayed, Ali H.
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Cites_doi 10.1109/TAC.1977.1101552
10.1109/9.481606
10.1016/0005-1098(95)00073-6
10.1109/78.485923
10.1109/9.29425
10.1109/9.481605
10.1109/TIT.1984.1056884
10.1109/79.295229
10.1016/0165-1684(96)00022-9
10.1109/78.506609
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Issue 1
Keywords 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
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Snippet This paper proposes a feedback structure for the design of l 2-stable algorithms for nonlinear adaptive filtering and identification, and establishes explicit...
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StartPage 13
SubjectTerms 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
Title An l2-stable feedback structure for nonlinear adaptive filtering and identification
URI https://dx.doi.org/10.1016/S0005-1098(96)00136-7
Volume 33
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