Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays

Jinde Cao; Ying Wan

doi:10.1016/j.neunet.2014.02.003

Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays

Neural Netw. 2014 May:53:165-72. doi: 10.1016/j.neunet.2014.02.003. Epub 2014 Feb 18.

Authors

Jinde Cao¹, Ying Wan²

Affiliations

¹ Department of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing 210096, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: jdcao@seu.edu.cn.
² Department of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing 210096, China.

PMID: 24632220
DOI: 10.1016/j.neunet.2014.02.003

Abstract

A single inertial BAM neural network with time-varying delays and external inputs is concerned in this paper. First, by choosing suitable variable substitution, the original system can be transformed into first-order differential equations. Then, we present several sufficient conditions for the global exponential stability of the equilibrium by using matrix measure and Halanay inequality, these criteria are simple in form and easy to verify in practice. Furthermore, when employing an error-feedback control term to the response neural network, parallel criteria regarding to the exponential synchronization of the drive-response neural network are also generated. Finally, some examples are given to illustrate our theoretical results.

Keywords: Global exponential stability; Global synchronization; Inertial BAM neural network; Matrix measure; Time-varying delays.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Feedback
Neural Networks, Computer*
Time Factors