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Project Euclid
Other literature type . 2006
Data sources: Project Euclid
Communications in Information and Systems
Article . 2006 . Peer-reviewed
Data sources: Crossref
DBLP
Article . 2006
Data sources: DBLP
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Recursive system identification by stochastic approximation

Authors: Chen, Han-Fu;

Recursive system identification by stochastic approximation

Abstract

The convergence theorems for the stochastic approximation (SA) algorithm with expanding truncations are first presented, which the system identification methods discussed in the paper are essentially based on. Then, the recursive identification algorithms are respectively defined for the multivariate errors-in-variables systems, Hammerstein systems, and Wiener systems. All es- timates given in the paper are strongly consistent.

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Keywords

errors-in-variables (EIV), recursive algorithm, stochastic approximation, Hammerstein system, strong consistency, Wiener system, System identification

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
Green
bronze