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Quantized Sampled-Data Control for Synchronization of Inertial Neural Networks With Heterogeneous Time-Varying Delays.

Authors :
Zhang, Ruimei
Zeng, Deqiang
Park, Ju H.
Liu, Yajuan
Zhong, Shouming
Source :
IEEE Transactions on Neural Networks & Learning Systems. Dec2018, Vol. 29 Issue 12, p6385-6395. 11p.
Publication Year :
2018

Abstract

This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov–Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2162237X
Volume :
29
Issue :
12
Database :
Academic Search Index
Journal :
IEEE Transactions on Neural Networks & Learning Systems
Publication Type :
Periodical
Accession number :
133211407
Full Text :
https://doi.org/10.1109/TNNLS.2018.2836339