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author:

Sang, H. (Sang, H..) [1] | Zheng, W. (Zheng, W..) [2] | Liu, Y. (Liu, Y..) [3] | Wang, P. (Wang, P..) [4] | Dimirovski, G.M. (Dimirovski, G.M..) [5]

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Abstract:

This paper performs a finite-time analysis (FTA) and implements the finite-time anti-disturbance synchronization (FTADS) control for general switched delayed neural networks (SDNNs) in a continuous-time context. While the prevailing results for SDNNs typically mandate the finite-time stability of individual subnetworks, this investigation explores a more inclusive scenario in which all subnetworks of SDNNs may exhibit finite-time instability. To address this, an innovative and less conservative FTA framework is established by developing a novel time-dependent multiple Lyapunov-Krasovskii functional (TDMLF) approach and incorporating a ranged dwell time (RDT) switching strategy. For SDNNs influenced by multiple disturbances, a FTADS criterion is established within the proposed finite-time design framework, demonstrating that the relevant synchronization error can achieve the desired finite-time boundedness. In the end, the reliability and superiority of the presented FTA approach and anti-disturbance synchronization control technique are thoroughly substantiated via two demonstration examples. © 2025 Elsevier Inc.

Keyword:

Anti-disturbance synchronization Finite-time boundedness Finite-time stability Ranged dwell time Switched delayed neural networks

Community:

  • [ 1 ] [Sang H.]College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China
  • [ 2 ] [Zheng W.]College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China
  • [ 3 ] [Liu Y.]College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China
  • [ 4 ] [Wang P.]School of Mathematics and Statistics, Fuzhou University, Fuzhou, 350108, China
  • [ 5 ] [Dimirovski G.M.]Doctoral School FEIT, SS Cyril and Methodius University, Skopje, 1000, North Macedonia

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Source :

Information Sciences

ISSN: 0020-0255

Year: 2025

Volume: 718

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JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 3

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