On the stability and Lyapunov direct method for fractional difference model of BAM neural networks-Reference-Cited by-同舟云学术

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On the stability and Lyapunov direct method for fractional difference model of BAM neural networks

Published:2020-03-04 Issue:3 Volume:38 Page:2491-2501
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Alzabut Jehad¹,Tyagi Swati²³,Martha S.C.³

Affiliation:

1. Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, Saudi Arabia

2. Department of Applied Sciences, Punjab Engineering College (PEC), Deemed to be University, Chandigarh, India

3. Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference41 articles.

1. Applications of Fractional Calculus in Physics

2. Recent applications of fractional calculus to science and engineering;Debnath;Int J Math Math Sci,2003

3. The Analysis of Fractional Differential Equations

4. An analytical solution for fractional oscillator in a resisting medium;Ismail;Chaos, Solitons & Fractals,2020

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1. Impulsive Boundedness for Nonautonomous Dynamic Complex Networks with Constraint Nonlinearity;Applied Mathematical Modelling;2023-03

2. A simple method for studying asymptotic stability of discrete dynamical systems and its applications;An International Journal of Optimization and Control: Theories & Applications (IJOCTA);2023-01-23

3. Time-dependent and Caputo derivative order-dependent quasi-uniform synchronization on fuzzy neural networks with proportional and distributed delays;Mathematics and Computers in Simulation;2023-01

4. A Caputo discrete fractional-order thermostat model with one and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz-type inequality;Journal of Inequalities and Applications;2022-05-08

5. A novel fixed-time stability strategy and its application to fixed-time synchronization control of semi-Markov jump delayed neural networks;Neurocomputing;2021-09

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