Dynamical analysis of neural networks with time-varying delays using the LMI approach Lakshmanan, Shanmugam, Lim, C. P., Bhatti, Asim, Gao, David and Nahavandi, Saeid 2015, Dynamical analysis of neural networks with time-varying delays using the LMI approach, in 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part III, Springer, New York, N.Y., pp. 297-305, doi: 10.1007/978-3-319-26555-1_34. Attached Files Name Description MIMEType Size Downloads Title Dynamical analysis of neural networks with time-varying delays using the LMI approach Author(s) Lakshmanan, Shanmugam orcid.org/0000-0002-4622-3782 Lim, C. P. orcid.org/0000-0003-4191-9083 Bhatti, Asim orcid.org/0000-0001-6876-1437 Gao, David Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey) Conference location Istanbul, Turkey Conference dates 9-12 Nov. 2015 Title of proceedings 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part III Publication date 2015 Series Neural Information Processing v.9491 Start page 297 End page 305 Total pages 9 Publisher Springer Place of publication New York, N.Y. Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Theory & Methods Computer Science Neural networks Interval time-varying delay Stability Linear matrix inequality STABILITY-CRITERIA DEPENDENT STABILITY Summary 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. ISBN 9783319265544 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-26555-1_34 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation 080109 Pattern Recognition and Data Mining 08 Information And Computing Sciences Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2015, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30080750 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 15 Jan 2016, 15:10:38 EST Sat, 16 Jan 2016, 04:01:16 EST Mon, 18 Jan 2016, 07:32:52 EST Thu, 31 Mar 2016, 09:29:54 EST Wed, 06 Apr 2016, 08:16:11 EST Wed, 20 Apr 2016, 11:30:52 EST Thu, 21 Apr 2016, 06:10:27 EST Thu, 19 May 2016, 09:57:54 EST Thu, 19 May 2016, 09:58:36 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 545 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Wed, 20 Apr 2016, 11:30:52 EST

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