Nonlinear measure approach for the robust exponential stability analysis of interval inertial Cohen–Grossberg neural networks |
| |
| Authors: | Ruoxia Li Jinde Cao Ahmed Alsaedi Bashir Ahmad Fuad E Alsaadi Tasawar Hayat |
| |
| Institution: | 1. Department of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, China;2. Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia;3. Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, King Abdulaziz University, Jeddah, 21589, Saudi Arabia;4. Electrical Computer Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia;5. Department of Mathematics, Quaid‐I‐Azam University, Islamabad, Pakistan |
| |
| Abstract: | This article is concerned with the existence and robust stability of an equilibrium point that related to interval inertial Cohen–Grossberg neural networks. Such condition requires the existence of an equilibrium point to a given system, so the existence and uniqueness of the equilibrium point are emerged via nonlinear measure method. Furthermore, with the help of Halanay inequality lemma, differential mean value theorem as well as inequality technique, several sufficient criteria are derived to ascertain the robust stability of the equilibrium point for the addressed system. The results obtained in this article will be shown to be new and they can be considered alternative results to previously results. Finally, the effectiveness and computational issues of the two models for the analysis are discussed by two examples. © 2016 Wiley Periodicals, Inc. Complexity 21: 459–469, 2016 |
| |
| Keywords: | interval Cohen– Grossberg neural networks robust stability inertial term nonlinear measure Halanay inequality |
|
|
