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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2017

07.04.2017

Robust State Estimation for Delayed Complex-Valued Neural Networks

verfasst von: Weiqiang Gong, Jinling Liang, Xiu Kan, Xiaobing Nie

Erschienen in: Neural Processing Letters | Ausgabe 3/2017

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Abstract

This paper is concerned with the state estimation problem for the uncertain complex-valued neural networks with time delays. The parameter uncertainties are assumed to be norm-bounded. Through available output measurements containing nonlinear Lipschitz-like terms, we aim to design a state estimator to estimate the complex-valued network such that, for all admissible parameter uncertainties and time delay, the dynamics of the error-state system is guaranteed to be globally asymptotically stable. In addition, the case that there are no parameter uncertainties is also considered. By utilizing the Lyapunov functional method and matrix inequality techniques, some sufficient delay-dependent criteria are derived to assure the existence of the desired estimator gains. Finally, two numerical examples with simulations are presented to demonstrate the effectiveness of the proposed estimation schemes.
Vorheriger Artikel Asymptotic Stability of Cohen–Grossberg BAM Neutral Type Neural Networks with Distributed Time Varying Delays
Nächster Artikel Towards Safe Semi-supervised Classification: Adjusted Cluster Assumption via Clustering

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Metadaten
Titel
Robust State Estimation for Delayed Complex-Valued Neural Networks
verfasst von
Weiqiang Gong
Jinling Liang
Xiu Kan
Xiaobing Nie
Publikationsdatum
07.04.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9626-2

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