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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Vilnius University Press
2019-11-01
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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.
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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 |
_version_ |
1724923785045344256 |