Input-to-state stability analysis for memristive Cohen-Grossberg-type neural networks with variable time delays

DOI10.1016/J.CHAOS.2018.07.021zbMath1415.34044OpenAlexW2887871071WikidataQ129440918 ScholiaQ129440918MaRDI QIDQ2000352

Lixia Duan, Yong Zhao, Juergen Kurths

Publication date: 28 June 2019

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2018.07.021

zbMATH Keywords

nonsmooth analysisinput-to-state stabilityLyapunov methodmemristive neural networksvariable time delays

Mathematics Subject Classification ID

Ordinary differential equations with impulses (34A37) Global stability of solutions to ordinary differential equations (34D23)

Related Items (6)

Input-to-state stability analysis for memristive BAM neural networks with variable time delays ⋮ Finite-time synchronization of fractional-order complex-valued Cohen-Grossberg neural networks with mixed time delays and state-dependent switching ⋮ Stability analysis for memristor-based stochastic multi-layer neural networks with coupling disturbance ⋮ Event-triggered stabilisation for stochastic delayed differential systems with exogenous disturbances ⋮ Novel criteria of ISS analysis for delayed memristive simplified Cohen-Grossberg BAM neural networks ⋮ Synchronization for a class of complex-valued memristor-based competitive neural networks (CMCNNs) with different time scales

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• Global Attractivity in Delayed Hopfield Neural Network Models
• An analysis of global asymptotic stability of delayed Cohen-Grossberg neural networks via nonsmooth analysis

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