Autor:

Stanisławski, Rafał ; Latawiec, Krzysztof J.

Współtwórca:

Cordón, Oskar - ed. ; Kazienko, Przemysław - ed.

Tytuł:

Normalized finite fractional differences: Computational and accuracy breakthroughs

Podtytuł:

Hybrid and Ensemble Methods in Machine Learning

Tytuł publikacji grupowej:

AMCS, Volume 22 (2012)

Temat i słowa kluczowe:

fractional difference ; Grünwald-Letnikov ; difference ; stability analysis ; recursive computation ; adaptive systems

Abstract:

This paper presents a series of new results in finite and infinite-memory modeling of discrete-time fractional differences. The introduced ?normalized finite fractional difference? is shown to properly approximate its fractional difference original, in particular in terms of the steady-state properties. ; A stability analysis is also presented and a recursive computation algorithm is offered for finite fractional differences. A thorough analysis of computational and accuracy aspects is culminated with the introduction of a ?perfect finite fractional difference? and, in particular, a powerful ?adaptive finite fractional difference?, whose excellent performance is illustrated in simulation examples.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2012

Typ zasobu:

artykuł

DOI:

10.2478/v10006-012-0067-9

Strony:

907-919

Źródło:

AMCS, Volume 22, Number 4 (2012) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

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Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego