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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2023

04.06.2022

DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems

verfasst von: Dechao Chen, Shuai Li

Erschienen in: Neural Processing Letters | Ausgabe 1/2023

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Abstract

Because of complexity during real-time synchronization of chaotic systems in practical applications, the convergence process with a definable-time (or to say, finite-time) property is urgently needed. To amend convergent property during real-time synchronization of chaotic system, this paper proposes a novel definable-convergence-time (DCT) neurodynamic approach for designing the associated neural network, called DCT-Net. Quite differing from the conventional zeroing neurodynamic (CZN) approach showing undefinable convergent time, the DCT-Net distinctively illustrates superiority of DCT property making synchronization of chaotic system faster and higher precision. In addition, theorems about globally stable in addition to convergent property are rigorously proved in detail. Moreover, different simulative examples substantiate the validity of DCT-Net for real-time synchronization of chaotic system. Comprehensive comparisons with other existing nets further verify advantages. At last, different tests detailedly discover the influence on convergent property by selecting various user-defined parameters as well as initial state.
Vorheriger Artikel Global Context-Aware Feature Extraction and Visible Feature Enhancement for Occlusion-Invariant Pedestrian Detection in Crowded Scenes
Nächster Artikel Variational Diversity Maximization for Hierarchical Skill Discovery

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Metadaten
Titel
DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems
verfasst von
Dechao Chen
Shuai Li
Publikationsdatum
04.06.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10911-9

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