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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2020

12.11.2019

Finite-Time \(L_\infty \) Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals

verfasst von: N. Gunasekaran, M. Syed Ali, S. Pavithra

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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Abstract

This paper, by proposing a sampled-data control scheme, we investigate the finite-time \(L_\infty \) performance state estimation of recurrent neural networks. By constructing a novel Lyapunov functional, new stability and stabilization conditions are derived. By utilizing integral inequality techniques, sufficient LMI conditions are derived to ensure the finite-time stability of considered neural networks. Furthermore, finite-time observer gain analysis of recurrent neural networks is set up to measure its disturbance tolerance capability in the fixed time interval. Numerical examples are given to verify the effectiveness of the proposed approach.
Vorheriger Artikel Almost Automorphic Solutions in Distribution Sense of Quaternion-Valued Stochastic Recurrent Neural Networks with Mixed Time-Varying Delays
Nächster Artikel Parameters Sharing in Residual Neural Networks

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Metadaten
Titel
Finite-Time Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals
verfasst von
N. Gunasekaran
M. Syed Ali
S. Pavithra
Publikationsdatum
12.11.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10114-9

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