Non-fragile finite-time \(H_\infty\) state estimation of neural networks with distributed time-varying delay

DOI10.1016/j.jfranklin.2017.09.002zbMath1373.93331OpenAlexW2753135362MaRDI QIDQ2412292

S. Saravanan, M. Syed Ali, Quanxin Zhu

Publication date: 23 October 2017

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2017.09.002

zbMATH Keywords

neural networksreciprocally convex approachlinear matrix inequality (LMI) techniquemodified Lyapunov-Krasovskii functional\(H_\infty\) state estimationdistributed time-varying delayWirtingers integral inequality

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Estimation and detection in stochastic control theory (93E10) (H^infty)-control (93B36) Algebraic methods (93B25)

Related Items (9)

Delay-probability-dependent state estimation for neural networks with hybrid delays ⋮ Global Mittag-Leffler synchronization of discrete-time fractional-order neural networks with time delays ⋮ New optimization approach of state estimation for neural networks with mixed delays ⋮ Quasi-projective and complete synchronization of discrete-time fractional-order delayed neural networks ⋮ Further improvement of finite-time boundedness based nonfragile state feedback control for generalized neural networks with mixed interval time-varying delays via a new integral inequality ⋮ Mixed \(H\infty\) and passivity finite-time state estimation for neural networks under hybrid cyber-attacks ⋮ Finite-time boundedness control for nonlinear networked systems with randomly occurring multi-distributed delays and missing measurements ⋮ Robust \(\mathcal H_\infty\) control for nonhomogeneous Markovian jump systems subject to quantized feedback and probabilistic measurements ⋮ Fault-tolerant H_∞ filtering for fuzzy networked control systems with quantisation effects

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