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Dynamical analysis of neural networks with time-varying delays using the LMI approach

conference contribution
posted on 2015-01-01, 00:00 authored by Lakshmanan Shanmugam, Chee Peng LimChee Peng Lim, Asim BhattiAsim Bhatti, D Gao, Saeid NahavandiSaeid Nahavandi
This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions.

History

Event

Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)

Volume

9491

Series

Neural Information Processing

Pagination

297 - 305

Publisher

Springer

Location

Istanbul, Turkey

Place of publication

New York, N.Y.

Start date

2015-11-09

End date

2015-11-12

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319265544

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, Springer

Title of proceedings

22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part III