Reference : Signal prediction using fractional derivative models
Parts of books : Contribution to collective works
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/40757
Signal prediction using fractional derivative models
English
Skovranek, Tomas mailto [Technical University of Kosice (TUKE) > BERG Faculty]
Despotovic, Vladimir mailto [University of Belgrade > Technical Faculty in Bor]
Apr-2019
De Gruyter Reference
Handbook of Fractional Calculus with Applications
Bǎleanu, Dumitru
Mendes Lopes, António
Walter de Gruyter GmbH
Volume 8: Applications in Engineering, Life and Social Sciences, Part B
179-206
Yes
978-3-11-057192-9
Berlin/Boston
[en] In this chapter the linear prediction (LP) and its generalisation to fractional linear prediction (FLP) is described with the possible applications to one-dimensional (1D) and two-dimensional (2D) signals. Standard test signals, such as the sine wave, the square wave, and the sawtooth wave, as well as the real-data signals, such as speech, electrocardiogram and electroencephalogram are used for the numerical experiments for the 1D case, and grayscale images for the 2D case. The 1D FLP model is proposed to have a similar construction as the LP model, i.e. it uses linear combination of fractional derivatives with different values of the fractional order. The 2D FLP model uses linear combination of the fractional derivatives in two directions, horizontal and vertical. The scheme for the computation of the optimal predictor coefficients for both 1D and 2D FLP models is also provided. The performance of the proposed FLP models is compared to the performance of the LP models, confirming that the proposed FLP can be successfully applied in processing of 1D and 2D signals, giving comparable or better performance using the same or even smaller number of parameters.
Slovak Research and Development Agency ; Slovak Grant Agency for Science ; COST Action
http://hdl.handle.net/10993/40757
10.1515/9783110571929
https://www.degruyter.com/view/books/9783110571929/9783110571929-007/9783110571929-007.xml

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