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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2021

31.07.2020 | Original Article

Global synchronization of memristive hybrid neural networks via nonlinear coupling

verfasst von: Cheng-De Zheng, Lulu Zhang, Huaguang Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 7/2021

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Abstract

This paper probes into the synchronization for memristor-based hybrid neural networks via nonlinear coupling. At first, a new condition is established to judge whether quadratic functions are negative or not on a closed interval regardless of their concavity or convexity. Then, by utilizing Legendre orthogonal polynomials, a recent extended integral inequality with free matrices is popularized to get tighter lower bound of some integral terms. Next, based on a novel Lyapunov functional, by applying our new integral inequality with free matrices, linear convex combination method and the new criterion, a new delay-dependent condition is gained to reach the global synchronization for the considered neural networks. At last, an example is presented to account for the validity of our results.
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Metadaten
Titel
Global synchronization of memristive hybrid neural networks via nonlinear coupling
verfasst von
Cheng-De Zheng
Lulu Zhang
Huaguang Zhang
Publikationsdatum
31.07.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05166-1

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