Mittag-Leffler stability analysis of fractional discrete-time neural networks via fixed point technique

A class of semilinear fractional difference equations is introduced in this paper. The fixed point theorem is adopted to find stability conditions for fractional difference equations. The complete solution space is constructed and the contraction mapping is established by use of new equivalent sum...

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Main Authors: Guo-Cheng Wu, Thabet Abdeljawad, Jinliang Liu, Dumitru Baleanu, Kai-Teng Wu
Format: Article
Language:English
Published: Vilnius University Press 2019-11-01
Series:Nonlinear Analysis
Subjects:
Online Access:http://www.journals.vu.lt/nonlinear-analysis/article/view/14904
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spelling doaj-e346d4a1c887469c9571b91c30d121232020-11-25T02:09:26ZengVilnius University PressNonlinear Analysis1392-51132335-89632019-11-0124610.15388/NA.2019.24.6.14904Mittag-Leffler stability analysis of fractional discrete-time neural networks via fixed point techniqueGuo-Cheng Wu0Thabet Abdeljawad1Jinliang Liu2Dumitru Baleanu3Kai-Teng Wu4Neijiang Normal UniversityPrince Sultan UniversityNanjing University of Finance and EconomicsCankaya UniversityNeijiang Normal University A class of semilinear fractional difference equations is introduced in this paper. The fixed point theorem is adopted to find stability conditions for fractional difference equations. The complete solution space is constructed and the contraction mapping is established by use of new equivalent sum equations in form of a discrete Mittag-Leffler function of two parameters. As one of the application, finite-time stability is discussed and compared. Attractivity of fractional difference equations is proved, and Mittag-Leffler stability conditions are provided. Finally, the stability results are applied to fractional discrete-time neural networks with and without delay, which show the fixed point technique’s efficiency and convenience. http://www.journals.vu.lt/nonlinear-analysis/article/view/14904ractional difference equationsfractional discrete-time neural networksMittag-Leffler stability
collection DOAJ
language English
format Article
sources DOAJ
author Guo-Cheng Wu
Thabet Abdeljawad
Jinliang Liu
Dumitru Baleanu
Kai-Teng Wu
spellingShingle Guo-Cheng Wu
Thabet Abdeljawad
Jinliang Liu
Dumitru Baleanu
Kai-Teng Wu
Mittag-Leffler stability analysis of fractional discrete-time neural networks via fixed point technique
Nonlinear Analysis
ractional difference equations
fractional discrete-time neural networks
Mittag-Leffler stability
author_facet Guo-Cheng Wu
Thabet Abdeljawad
Jinliang Liu
Dumitru Baleanu
Kai-Teng Wu
author_sort Guo-Cheng Wu
title Mittag-Leffler stability analysis of fractional discrete-time neural networks via fixed point technique
title_short Mittag-Leffler stability analysis of fractional discrete-time neural networks via fixed point technique
title_full Mittag-Leffler stability analysis of fractional discrete-time neural networks via fixed point technique
title_fullStr Mittag-Leffler stability analysis of fractional discrete-time neural networks via fixed point technique
title_full_unstemmed Mittag-Leffler stability analysis of fractional discrete-time neural networks via fixed point technique
title_sort mittag-leffler stability analysis of fractional discrete-time neural networks via fixed point technique
publisher Vilnius University Press
series Nonlinear Analysis
issn 1392-5113
2335-8963
publishDate 2019-11-01
description A class of semilinear fractional difference equations is introduced in this paper. The fixed point theorem is adopted to find stability conditions for fractional difference equations. The complete solution space is constructed and the contraction mapping is established by use of new equivalent sum equations in form of a discrete Mittag-Leffler function of two parameters. As one of the application, finite-time stability is discussed and compared. Attractivity of fractional difference equations is proved, and Mittag-Leffler stability conditions are provided. Finally, the stability results are applied to fractional discrete-time neural networks with and without delay, which show the fixed point technique’s efficiency and convenience.
topic ractional difference equations
fractional discrete-time neural networks
Mittag-Leffler stability
url http://www.journals.vu.lt/nonlinear-analysis/article/view/14904
work_keys_str_mv AT guochengwu mittaglefflerstabilityanalysisoffractionaldiscretetimeneuralnetworksviafixedpointtechnique
AT thabetabdeljawad mittaglefflerstabilityanalysisoffractionaldiscretetimeneuralnetworksviafixedpointtechnique
AT jinliangliu mittaglefflerstabilityanalysisoffractionaldiscretetimeneuralnetworksviafixedpointtechnique
AT dumitrubaleanu mittaglefflerstabilityanalysisoffractionaldiscretetimeneuralnetworksviafixedpointtechnique
AT kaitengwu mittaglefflerstabilityanalysisoffractionaldiscretetimeneuralnetworksviafixedpointtechnique
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