state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

DOI10.1080/03081079.2015.1106731zbMath1342.93107OpenAlexW2343008377MaRDI QIDQ2817116

Hongjian Liu, Zidong Wang, Bo Shen, Fuad Eid S. Alsaadi

Publication date: 29 August 2016

Published in: International Journal of General Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03081079.2015.1106731

zbMATH Keywords

discrete timememristive neural networks\(H_\infty\) state estimationstochastic time-delays

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10) (H^infty)-control (93B36) Stochastic systems in control theory (general) (93E03)

Related Items (8)

Design of generalized dissipativity state estimator for static neural networks including state time delays and leakage delays ⋮ \(H_\infty\) state estimation of stochastic memristor-based neural networks with time-varying delays ⋮ An Arcak-type state estimation design for time-delayed static neural networks with leakage term based on unified criteria ⋮ \(H_\infty\) and \(l_2 - l_\infty\) finite-horizon filtering with randomly occurring gain variations and quantization effects ⋮ Delay-distribution-dependent \(H_\infty\) state estimation for delayed neural networks with \((x, v)\)-dependent noises and fading channels ⋮ Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse ⋮ \(H_\infty\) and \(l_2\)-\(l_\infty\) state estimation for delayed memristive neural networks on finite horizon: the round-robin protocol ⋮ Design of extended dissipativity state estimation for generalized neural networks with mixed time-varying delay signals

Cites Work

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• State estimation for static neural networks with time-varying delay
• An analysis of exponential stability of delayed neural networks with time varying delays
• Distributed \(\mathcal H_{\infty}\) state estimation with stochastic parameters and nonlinearities through sensor networks: the finite-horizon case
• Global exponential stability of generalized recurrent neural networks with discrete and distributed delays
• Global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays
• Object recognition by a Hopfield neural network
• Delay-derivative-dependent stability criterion for neural networks with probabilistic time-varying delay

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