Results 251 to 260 of about 151,567 (269)

Set-membership kernel adaptive algorithms

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities.
Andre Flores, Rodrigo C. de Lamare
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Set-membership binormalized data-reusing LMS algorithms

IEEE Transactions on Signal Processing, 2003
This paper presents and analyzes novel data selective normalized adaptive filtering algorithms with two data reuses. The algorithms [the set-membership binormalized LMS (SM-BN-DRLMS) algorithms] are derived using the concept of set-membership filtering (SMF).
P.S.R. Diniz, S. Werner
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Set membership prediction of nonlinear time series

Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2002
A nonlinear prediction method based on a set membership approach is proposed. Such method does not need any assumption about the functional form of the model used for prediction, but uses only some information on its regularity. On the contrary, most of the existing prediction methods need the choice of a model structure and this choice is usually the ...
MILANESE, Mario, NOVARA, Carlo
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Set-membership methodology for model-based prognosis

ISA Transactions, 2017
This paper addresses model-based prognosis to predict Remaining Useful Life (RUL) of a class of dynamical systems. The methodology is based on singular perturbed techniques to take into account the slow behavior of degradations. The full-order system is firstly decoupled into slow and fast subsystems.
Yousfi, Basma, Raissi, Tarek, Amairi, Messaoud, Aoun, Mohamed   +3 more
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Set-membership identification for adaptive control: input design

42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), 2004
An input design to guarantee the boundedness and the decreasing volume of the uncertainty set is proposed in the scenario of open-loop identification for control. The estimated system is described by a linear time-invariant SISO system of known order n with unknown-but-bounded modeling error in discrete time.
Cadic, M.A., Polderman, J.W., Mareels, I.M.Y.   +2 more
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XOR-Satisfiability Set Membership Filters

2018
Set membership filters are used as a primary test for whether large sets contain given elements. The most common such filter is the Bloom filter [6]. Most pertinent to this article is the recently introduced Satisfiability (SAT) filter [31]. This article proposes the XOR-Satisfiability filter, a variant of the SAT filter based on random k-XORSAT ...
Sean A. Weaver, Hannah J. Roberts, Michael J. Smith   +2 more
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Fault Detection Based on Set-Membership Inversion

IFAC Proceedings Volumes, 2006
This paper deals with a set-membership method for fault detection. Based on interval analysis, the proposed approach focuses on the design of consistency tests for dynamical systems with additive and multiplicative parameter uncertainties. Instead of canceling uncertainties following the example of the so-called robust approaches, uncertain analytical ...
Olivier Adrot, Stéphane Ploix
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Set-Membership Adaptive Filtering

2003
Based on a bounded-error assumption, set-membership adaptive filtering (SMAF) offers a viable alternative to traditional adaptive filtering techniques that are aimed to minimize an ensemble (or a time) average of the errors. This chapter presents an overview of the principles of SMAF, its features, and some applications. Highlighting the novel features
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Set-Membership Adaptive Filtering

2019
The families of adaptive filtering algorithms introduced so far present a trade-off between the speed of convergence and the misadjustment after the transient. These characteristics are easily observable in stationary environments. In general fast-converging algorithms tend to be very dynamic, a feature not necessarily advantageous after convergence in
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Hammerstein model identification with set membership errors

Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 2003
The problem of the Hammerstein dynamic system identification is considered when the measurement error is characterized in a set-membership context. The proposed approach accomplishes parameter identification through the introduction of a linearized augmented Hammerstein model whose parameter bounds allow us to derive overbounds to the Hammerstein model
G. Belforte, GAY, Paolo
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