Existence and stability of almost periodic solutions of Hopfield neural networks with continuously distributed delays

DOI10.1016/J.NA.2009.05.008zbMath1183.34113OpenAlexW2030453024MaRDI QIDQ732616

Chuanzhi Bai

Publication date: 9 October 2009

Published in: Nonlinear Analysis. Theory, Methods \& Applications. Series A: Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.na.2009.05.008

zbMATH Keywords

exponential stabilityalmost periodic solutionfixed point theoremHopfield neural networkscontinuously distributed delays

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Almost and pseudo-almost periodic solutions to functional-differential equations (34K14) Stability theory of functional-differential equations (34K20)

Related Items (11)

On the differential equations of recurrent neural networks ⋮ Almost periodic solution for memristive neural networks with time-varying delays ⋮ Almost periodic solution in a Lotka-Volterra recurrent neural networks with time-varying delays ⋮ Almost automorphic solutions for quaternion-valued Hopfield neural networks with mixed time-varying delays and leakage delays ⋮ A shunting inhibitory cellular neural network with continuously distributed delays of neutral type ⋮ Stability of unique pseudo almost periodic solutions with measure. ⋮ Exponential synchronization of delayed neutral-type neural networks with Lévy noise under non-Lipschitz condition ⋮ Stability analysis of almost periodic solutions of discontinuous BAM neural networks with hybrid time-varying delays and \(D\) operator ⋮ Almost periodic dynamical behaviors for generalized Cohen-Grossberg neural networks with discontinuous activations via differential inclusions ⋮ Oscillation and stability of multidirectional associative memory neural network with mixed delays ⋮ Stability of neutral-type neural network with Lévy noise and mixed time-varying delays

Cites Work

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• Delay-dependent exponential stability for a class of neural networks with time delays
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