Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method

In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is con...

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Bibliographic Details
Published in:Neural networks 2017-12, Vol.96, p.91-100
Main Authors: Li, Xuanying, Li, Xiaotong, Hu, Cheng
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Asymptotic stability
Feedback
Inertial neural network
Neural Networks (Computer)
Synchronization
Time Factors
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Summary:In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained. To reduce the control cost, an adaptive control scheme is designed to realize the asymptotic synchronization. It is noted that the dynamical behaviors of inertial neural networks are directly analyzed in this paper by constructing some new Lyapunov functionals, this is totally different from the traditional reduced-order variable substitution method. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2017.09.009