Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances

DOI10.1016/j.neunet.2014.02.007zbMath1307.93342OpenAlexW2066267825WikidataQ51103225 ScholiaQ51103225MaRDI QIDQ2339399

Yanyan Li

Publication date: 1 April 2015

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2014.02.007

zbMATH Keywords

time delaysrecurrent neural networksglobal exponential stabilityrandom disturbancesadjustable parameters

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Perturbations in control/observation systems (93C73) Asymptotic stability in control theory (93D20) Stochastic systems in control theory (general) (93E03)

Related Items (5)

Noise expresses exponential decay for globally exponentially stable nonlinear time delay systems ⋮ Noise further expresses exponential decay for globally exponentially stable time-varying delayed neural networks ⋮ Robustness analysis of hybrid stochastic neural networks with neutral terms and time-varying delays ⋮ Mean square stability of linear stochastic neutral‐type time‐delay systems with multiple delays ⋮ Robustness analysis of global exponential stability of nonlinear stochastic systems with respect to neutral terms and time-varying delays

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