Cordón, Oskar - ed. ; Kazienko, Przemysław - ed.
Hybrid and Ensemble Methods in Machine Learning
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.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, Volume 22, Number 4 (2012) ; kliknij tutaj, żeby przejść
Biblioteka Uniwersytetu Zielonogórskiego
2021-10-14
2021-09-15
174
https://zbc.uz.zgora.pl/publication/65817
Nazwa wydania | Data |
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Normalized finite fractional differences: Computational and accuracy breakthroughs | 2021-10-14 |
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