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Neural Processing Letters
Erschienen in: Neural Processing Letters 6/2022

29.04.2022

Robust Asymptotic Stability and Projective Synchronization of Time-Varying Delayed Fractional Neural Networks Under Parametric Uncertainty

verfasst von: Mengqi Li, Xujun Yang, Qiankun Song, Xiaofeng Chen

Erschienen in: Neural Processing Letters | Ausgabe 6/2022

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Abstract

In this paper, the robust asymptotic stability and projective synchronization of fractional-order time-varying delayed neural networks with uncertain parameters are studied. On account of the homeomorphism mapping theorem, free-weighting method and generalized Halanay inequality, several sufficient conditions of existence, uniqueness and asymptotic stability of the equilibrium point of the addressed models in the form of LMIs are established. In addition, some criteria ensuring the robust asymptotic projective synchronization between the master system and the slave system are deduced based on a suitable controller. Finally, two numerical simulations are designed to illustrate the effectiveness and rationality of the theoretical results.
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Metadaten
Titel
Robust Asymptotic Stability and Projective Synchronization of Time-Varying Delayed Fractional Neural Networks Under Parametric Uncertainty
verfasst von
Mengqi Li
Xujun Yang
Qiankun Song
Xiaofeng Chen
Publikationsdatum
29.04.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 6/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10825-6

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