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Journal of the Franklin Institute
Article . 2017 . Peer-reviewed
License: Elsevier TDM
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Gradient iterative algorithm for dual-rate nonlinear systems based on a novel particle filter

descriptionPublicationkeyboard_double_arrow_right Article 01 Jul 2017 English Publisher:Elsevier BVJournal:Journal of the Franklin Institute, volume 354, pages 4,425-4,437 (issn: 0016-0032, Copyright policy )
Authors: Jing Chen; Juan Li; Yanjun Liu;

doi: 10.1016/j.jfranklin.2017.04.003

Gradient iterative algorithm for dual-rate nonlinear systems based on a novel particle filter

Abstract

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Keywords

Sampled-data control/observation systems, dual-rate nonlinear systems, Estimation and detection in stochastic control theory, Identification in stochastic control theory, Computational methods in stochastic control, novel particle filter, identification, gradient iterative algorithm, slow sampled rate, Filtering in stochastic control theory

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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!
36
Top 10%
Top 10%
Top 10%
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