\(H_\infty\) state estimator design for discrete-time switched neural networks with multiple missing measurements and sojourn probabilities

DOI10.1016/j.jfranklin.2015.10.017zbMath1336.93153OpenAlexW2140726079MaRDI QIDQ285718

Yanzheng Zhu, Rajan Rakkiyappan, R. Sasirekha, Lixian Zhang

Publication date: 19 May 2016

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2015.10.017

zbMATH Keywords

\(H_\infty\) state estimatorasymptotic mean square stabilitydiscrete-time switched neural networksLyapunov-Krasovskii functional (LKF)multiple missing measurements and sojourn probabilitiessecond order reciprocally convex technique

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 stability in control theory (93E15)

Related Items (6)

Event-triggered \(H_\infty\) filtering control for a class of distributed parameter systems with Markovian switching topology ⋮ Exponential Stabilization of Switched Discrete‐Time Systems with All Unstable Modes ⋮ Fuzzy-model-based \(\mathcal{H}_\infty\) control for discrete-time switched systems with quantized feedback and unreliable links ⋮ Variance-constrained resilient \(H_{\infty }\) state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements ⋮ Finite-time event-triggered approach for recurrent neural networks with leakage term and its application ⋮ Extended dissipative resilient estimator design for discrete-time switched neural networks with unreliable links

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