This submission contains functions implementing the fractional linear prediction (FLP) models to estimate the one-dimensional signal. Two versions of FLP are implemented.
The first approach to FLP is using the whole history of the signal ("full" memory) - function flp_f.m. The second approach to FLP uses the "restricted" memory (restricted to two, three or four previous samples) - function flp_r.m.
The methods, models and applications implemented in the form of the proposed functions were presented in the works , . The MATLAB implementation of the discretization of the fractional order derivative can be found in .
 Tomas Skovranek, Vladimir Despotovic: Signal prediction using fractional derivative models. In: Handbook of Fractional Calculus with Applications, Volume 8: Applications in Engineering, Life and Social Sciences, Part B, Pages 179-205. De Gruyter, 2019.
 Vladimir Despotovic, Tomas Skovranek, Zoran Peric: One-parameter fractional linear prediction, Computers & Electrical Engineering, vol. 69, July 2018, Pages 158-170 (Included in Special Issue on Signal Processing, March 2018).
 Igor Podlubny, Tomas Skovranek, Blas M. Vinagre Jara: Matrix approach to discretization of ODEs and PDEs of arbitrary real order, MathWorks, Inc., Matlab Central File Exchage, 2008 (Updated 04 Mar 2016).
Tomas Skovranek, Vladimir Despotovic (2021). Fractional Linear Prediction (https://www.mathworks.com/matlabcentral/fileexchange/67867-fractional-linear-prediction), MATLAB Central File Exchange. Retrieved .
Dear Sibghatullah Khan Khan > ban.m and bcrecur.m functions, that are the part of the FEX package "Matrix approach to discretization of ODEs and PDEs of arbitrary real order (22071)", are used in this toolbox. Please download them before using this package.
Undefined function or variable 'ban'.
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