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

Dechao Chen Orcid Logo, Shuai Li Orcid Logo

Neural Processing Letters, Volume: 55, Issue: 1, Pages: 819 - 837

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1007/s11063-022-10911-9

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 propose...

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Published in: Neural Processing Letters
ISSN: 1370-4621 1573-773X
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa60254
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first_indexed 2022-06-28T15:01:08Z
last_indexed 2023-01-13T19:20:15Z
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spelling v2 60254 2022-06-16 DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2022-06-16 MECH 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. Journal Article Neural Processing Letters 55 1 819 837 Springer Science and Business Media LLC 1370-4621 1573-773X Zeroing neurodynamic approach; Definable convergence time; Real-time synchronization; DCT-Net; Chaotic systems 1 2 2023 2023-02-01 10.1007/s11063-022-10911-9 http://dx.doi.org/10.1007/s11063-022-10911-9 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University This work is supported by the National Natural Science Foundation of China (with number 61906054), by Zhejiang Provincial Natural Science Foundation of China (with number LY21-F030006). 2023-06-29T16:33:41.3561379 2022-06-16T08:42:29.1828665 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Dechao Chen 0000-0002-4817-4531 1 Shuai Li 0000-0001-8316-5289 2
title DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems
spellingShingle DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems
Shuai Li
title_short DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems
title_full DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems
title_fullStr DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems
title_full_unstemmed DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems
title_sort DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Dechao Chen
Shuai Li
format Journal article
container_title Neural Processing Letters
container_volume 55
container_issue 1
container_start_page 819
publishDate 2023
institution Swansea University
issn 1370-4621
1573-773X
doi_str_mv 10.1007/s11063-022-10911-9
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
url http://dx.doi.org/10.1007/s11063-022-10911-9
document_store_str 0
active_str 0
description 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.
published_date 2023-02-01T16:33:36Z
_version_ 1770051670272638976
score 11.036706

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