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

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 glob...

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Bibliographic Details
Published in:Neural networks 2014-05, Vol.53, p.165-172
Main Authors: Cao, Jinde, Wan, Ying
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Feedback
Global exponential stability
Global synchronization
Inertial BAM neural network
Matrix measure
Neural Networks (Computer)
Nonlinear dynamics and nonlinear dynamical systems
Physics
Statistical physics, thermodynamics, and nonlinear dynamical systems
Synchronization
coupled oscillators
Time Factors
Time-varying delays
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2014.02.003