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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2018

21.11.2017

Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters

verfasst von: Tengda Wei, Yangfan Wang, Linshan Wang

Erschienen in: Neural Processing Letters | Ausgabe 2/2018

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Abstract

This paper investigates robust exponential synchronization for stochastic delayed neural networks with reaction–diffusion terms and Markovian jumping parameters driven by infinite dimensional Wiener processes. The novelty of this paper lives in the use of a new Lyapunov–Krasovskii functional and Poincaré inequality to present some criteria for robust exponential synchronization in terms of linear matrix inequalities (LMIs) and matrix measure under Robin boundary conditions. Finally, two numerical examples are provided to illustrate the effectiveness of the easily verifiable synchronization LMIs in MATLAB toolbox.
Vorheriger Artikel A New Conjugate Gradient Method with Smoothing Regularization Based on a Modified Secant Equation for Training Neural Networks
Nächster Artikel Global Asymptotic Stability for Complex-Valued Neural Networks with Time-Varying Delays via New Lyapunov Functionals and Complex-Valued Inequalities

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Metadaten
Titel
Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters
verfasst von
Tengda Wei
Yangfan Wang
Linshan Wang
Publikationsdatum
21.11.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9756-6

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