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, ShanmugamORCID iD for Lakshmanan, Shanmugam orcid.org/0000-0002-4622-3782
Lim, C. P.ORCID iD for Lim, C. P. orcid.org/0000-0003-4191-9083
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Gao, David
Nahavandi, SaeidORCID iD for 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
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 286 Abstract Views, 4 File Downloads  -  Detailed Statistics
Created: Wed, 20 Apr 2016, 11:30:52 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.