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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/42297
Title: Exponential dissipativity criteria for generalized BAM neural networks with variable delays
Authors: R. Saravanakumar
Grienggrai Rajchakit
M. Syed Ali
Young Hoon Joo
Mahidol University
Maejo University
Thiruvalluvar University
Kunsan National University
Keywords: Computer Science
Issue Date: 11-Nov-2017
Citation: Neural Computing and Applications. (2017), 1-10
Abstract: © 2017 The Natural Computing Applications Forum This article evaluates the exponential dissipativity and passivity criterions for generalized bidirectional associative memory neural networks (BAMGNNs) including interval time-varying delayed signals. Exponential dissipativity and passivity criterions are proposed by making suitable Lyapunov–Krasovskii functional and proposing a novel approach. The improved reciprocally convex combination and weighted integral inequality techniques are utilized to obtain new exponential dissipativity and passivity conditions of such delayed BAMGNNs. The feasibility of the obtained results is clearly demonstrated by numerical examples.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85033467531&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/42297
ISSN: 09410643
Appears in Collections:Scopus 2016-2017

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