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Cognitive Neurodynamics
Erschienen in: Cognitive Neurodynamics 2/2023

12.07.2022 | Research Article

\(H_{\infty }\) state estimation of quaternion-valued inertial neural networks: non-reduced order method

verfasst von: Zhengwen Tu, Nina Dai, Liangwei Wang, Xinsong Yang, Yanqiu Wu, Ning Li, Jinde Cao

Erschienen in: Cognitive Neurodynamics | Ausgabe 2/2023

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Abstract

This paper concentrates on the problem of \(H_{\infty }\) state estimation for quaternion-valued inertial neural networks (QVINNs) with nonidentical time-varying delay. Without reducing the original second order system into two first order systems, a non-reduced order method is developed to investigate the addressed QVINNs, which is different from the majority of existing references. By constructing a new Lyapunov functional with tuning parameters, some easily checked algebraic criteria are established to ascertain the asymptotic stability of error-state system with the desired \(H_{\infty }\) performance. Moreover, an effective algorithm is provided to design the estimator parameters. Finally, a numerical example is given out to illustrate the feasibility of the designed state estimator.
Vorheriger Artikel Delay-dependent transitions of phase synchronization and coupling symmetry between neurons shaped by spike-timing-dependent plasticity
Nächster Artikel The architecture of functional brain network modulated by driving during adverse weather conditions

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Metadaten
Titel
state estimation of quaternion-valued inertial neural networks: non-reduced order method
verfasst von
Zhengwen Tu
Nina Dai
Liangwei Wang
Xinsong Yang
Yanqiu Wu
Ning Li
Jinde Cao
Publikationsdatum
12.07.2022
Verlag
Springer Netherlands
Erschienen in
Cognitive Neurodynamics / Ausgabe 2/2023
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-022-09835-w

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