PMID- 35797316
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240217
IS  - 2162-2388 (Electronic)
IS  - 2162-237X (Linking)
VI  - 35
IP  - 2
DP  - 2024 Feb
TI  - Matrix Measure-Based Event-Triggered Impulsive Quasi-Synchronization on Coupled 
      Neural Networks.
PG  - 1821-1832
LID - 10.1109/TNNLS.2022.3185586 [doi]
AB  - In this article, the quasi-synchronization for a kind of coupled neural networks 
      with time-varying delays is investigated via a novel event-triggered impulsive 
      control approach. In view of the randomly occurring uncertainties (ROUs) in the 
      communication channels, the global quasi-synchronization for the coupled neural 
      networks within a given error bound is considered instead of discussing the 
      complete synchronization. A kind of distributed event-triggered impulsive 
      controllers is presented with considering the Bernoulli stochastic variables 
      based on ROUs, which works at each event-triggered impulsive instant. According 
      to the matrix measure method and the Lyapunov stability theorem, several 
      sufficient conditions for the realization of the quasi-synchronization are 
      successfully derived. Combining with the mathematical methodology with the 
      formula of variation of parameters and the comparison principle for the impulsive 
      systems with time-varying delays, the convergence rate and the synchronization 
      error bound are precisely estimated. Meanwhile, the Zeno behaviors could be 
      eliminated in the coupled neural network with the proposed event-triggered 
      function. Finally, a numerical example is presented to prove the results of 
      theoretical analysis.
FAU - Jiang, Chenhui
AU  - Jiang C
FAU - Tang, Ze
AU  - Tang Z
FAU - Park, Ju H
AU  - Park JH
FAU - Feng, Jianwen
AU  - Feng J
LA  - eng
PT  - Journal Article
DEP - 20240205
PL  - United States
TA  - IEEE Trans Neural Netw Learn Syst
JT  - IEEE transactions on neural networks and learning systems
JID - 101616214
SB  - IM
EDAT- 2022/07/08 06:00
MHDA- 2022/07/08 06:01
CRDT- 2022/07/07 13:34
PHST- 2022/07/08 06:01 [medline]
PHST- 2022/07/08 06:00 [pubmed]
PHST- 2022/07/07 13:34 [entrez]
AID - 10.1109/TNNLS.2022.3185586 [doi]
PST - ppublish
SO  - IEEE Trans Neural Netw Learn Syst. 2024 Feb;35(2):1821-1832. doi: 
      10.1109/TNNLS.2022.3185586. Epub 2024 Feb 5.