Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses. Academic Article

abstract

• In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Zhang, W., Huang, T., He, X., & Li, C.

citation count

• 60

complete list of authors

• Zhang, Wei||Huang, Tingwen||He, Xing||Li, Chuandong

publication date

• November 2017

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Delayed Impulsive
• Exponential Stability
• Impulsive
• Inertial Memristor-based Neural Network
• Machine Learning
• Neural Networks, Computer
• Time Delay
• Time Factors

PubMed Central ID

• 28938129

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2017.03.012

start page

• 102

end page

• 109

volume

• 95

URL

• http://dx.doi.org/10.1016/j.neunet.2017.03.012