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:
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:
Typ zasobu:
DOI:
Strony:
Źródło:
AMCS, Volume 22, Number 4 (2012) ; kliknij tutaj, żeby przejść