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Type: Journal article
Title: Exponential synchronization of neural networks with discrete and distributed delays under time-varying sampling
Author: Wu, Z.
Shi, P.
Su, H.
Chu, J.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2012; 23(9):1368-1376
Publisher: IEEE
Issue Date: 2012
ISSN: 2162-237X
Statement of
Zheng-Guang Wu, Peng Shi, Hongye Su, and Jian Chu
Abstract: This paper investigates the problem of master-slave synchronization for neural networks with discrete and distributed delays under variable sampling with a known upper bound on the sampling intervals. An improved method is proposed, which captures the characteristic of sampled-data systems. Some delay-dependent criteria are derived to ensure the exponential stability of the error systems, and thus the master systems synchronize with the slave systems. The desired sampled-data controller can be achieved by solving a set of linear matrix inequalitys, which depend upon the maximum sampling interval and the decay rate. The obtained conditions not only have less conservatism but also have less decision variables than existing results. Simulation results are given to show the effectiveness and benefits of the proposed methods.
Keywords: Exponential synchronization; linear matrix inequality (LMI); neural networks; sampled-data control.
Rights: © 2012 IEEE
RMID: 0020127331
DOI: 10.1109/TNNLS.2012.2202687
Appears in Collections:Electrical and Electronic Engineering publications

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