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Exponential stabilization of non-autonomous delayed neural networks via Riccati equations

journal contribution
posted on 2014-11-01, 00:00 authored by M V Thuan, Levan Hien, V N Phat
This paper concerns with the problem of exponential stabilization for a class of non-autonomous neural networks with mixed discrete and distributed time-varying delays. Two cases of discrete time-varying delay, namely (i) slowly time-varying; and (ii) fast time-varying, are considered. By constructing an appropriate Lyapunov-Krasovskii functional in case (i) and utilizing the Razumikhin technique in case (ii), we establish some new delay-dependent conditions for designing a memoryless state feedback controller which stabilizes the system with an exponential convergence of the resulting closed-loop system. The proposed conditions are derived through solutions of some types of Riccati differential equations. Applications to control a class of autonomous neural networks with mixed time-varying delays are also discussed in this paper. Some numerical examples are provided to illustrate the effectiveness of the obtained results.

History

Journal

Applied mathematics and computation

Volume

246

Pagination

533 - 545

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0096-3003

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal; C Journal article

Copyright notice

2014, Elsevier