Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with uncertain transition probabilities

DOI10.1016/j.neunet.2020.06.023zbMath1478.93679OpenAlexW3040215347WikidataQ97564763 ScholiaQ97564763MaRDI QIDQ2057702

Zidong Wang, Weiyin Fei, Hongli Dong, Jiahui Li

Publication date: 7 December 2021

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2020.06.023

zbMATH Keywords

Markov chainartificial neural networksuncertain transition probabilityrandom delaysstochastic communication protocol

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Applications of continuous-time Markov processes on discrete state spaces (60J28) Delay control/observation systems (93C43) Networked control (93B70)

Related Items (8)

\(H_\infty\) master-slave synchronization for delayed impulsive implicit hybrid neural networks based on memory-state feedback control ⋮ Almost periodic synchronization of quaternion-valued fuzzy cellular neural networks with leakage delays ⋮ Scalable consensus filtering for uncertain systems over sensor networks with Round‐Robin protocol ⋮ \(H_\infty\) and \(l_2\)-\(l_\infty\) state estimation for delayed memristive neural networks on finite horizon: the round-robin protocol ⋮ Dynamic event-based state estimation for delayed artificial neural networks with multiplicative noises: a gain-scheduled approach ⋮ Multi-sensor filtering fusion meets censored measurements under a constrained network environment: advances, challenges and prospects ⋮ On ℓ₂–ℓ∞ output-feedback control scheduled by stochastic communication protocol for two-dimensional switched systems ⋮ Communication-protocol-based analysis and synthesis of networked systems: progress, prospects and challenges

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