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Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays

Rajivganthi, C, Rihan, FA, Lakshmanan, Shanmugam and Muthukumar, P 2016, Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays, Neural computing and applications, pp. 1-12, doi: 10.1007/s00521-016-2641-9.

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Title Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays
Author(s) Rajivganthi, C
Rihan, FA
Lakshmanan, ShanmugamORCID iD for Lakshmanan, Shanmugam orcid.org/0000-0002-4622-3782
Muthukumar, P
Journal name Neural computing and applications
Start page 1
End page 12
Total pages 12
Publisher Springer Verlag
Place of publication Berlin, Germany
Publication date 2016-11-01
ISSN 0941-0643
1433-3058
Keyword(s) Banach contraction principle
Cohen– Grossberg BAM neural networks
Finite-time stability
Fractional-order derivative
Time delay
Summary In this paper, the problem of finite-time stability for a class of fractional-order Cohen–Grossberg BAM neural networks with time delays is investigated. Using some inequality techniques, differential mean value theorem and contraction mapping principle, sufficient conditions are presented to ensure the finite-time stability of such fractional-order neural models. Finally, a numerical example and simulations are provided to demonstrate the effectiveness of the derived theoretical results.
Notes In press
Language eng
DOI 10.1007/s00521-016-2641-9
Field of Research 0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, The Natural Computing Applications Forum
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089103

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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