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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 NahavandiThis 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
9491Series
Neural Information ProcessingPagination
297 - 305Publisher
SpringerLocation
Istanbul, TurkeyPlace of publication
New York, N.Y.Publisher DOI
Start date
2015-11-09End date
2015-11-12ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319265544Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, SpringerTitle of proceedings
22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part IIIUsage metrics
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