A new approach based on discrete-time high-order neural networks with delays and impulses

DOI10.1016/j.jfranklin.2018.04.032zbMath1390.93672OpenAlexW2800032730MaRDI QIDQ1648069

Cheng Hu, Jinling Wang, Tianlong Ma, Haijun Jiang

Publication date: 27 June 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2018.04.032

zbMATH Keywords

impulsesexponential stabilityasymptotic stabilitydelaysdiscrete-time high-order neural networks (HONNs)

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Discrete-time control/observation systems (93C55) Asymptotic stability in control theory (93D20) Robust stability (93D09)

Related Items (8)

Global exponential stability of high-order Hopfield neural networks with state-dependent impulses ⋮ Periodic solutions of Cohen-Grossberg-type bi-directional associative memory neural networks with neutral delays and impulses ⋮ A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen-Grossberg neural networks ⋮ Global exponential stability of discrete-time Hopfield neural network models with unbounded delays ⋮ Delayed impulsive control for lag synchronization of neural networks with time-varying delays and partial unmeasured states ⋮ Global exponential synchronization of discrete-time high-order switched neural networks and its application to multi-channel audio encryption ⋮ Synchronization for fractional-order discrete-time neural networks with time delays ⋮ Global exponential stability of discrete-time higher-order Cohen-Grossberg neural networks with time-varying delays, connection weights and impulses

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