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Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals

Authors
Rajchakit, G.Saravanakumar, R.Ahn, Choon KiKarimi, Hamid Reza
Issue Date
2월-2017
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Generalized neural network; Stability analysis; Time-varying delay; Weighted integral inequality
Citation
NEURAL NETWORKS, v.86, pp.10 - 17
Indexed
SCIE
SCOPUS
Journal Title
NEURAL NETWORKS
Volume
86
Start Page
10
End Page
17
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/84827
DOI
10.1016/j.neunet.2016.10.009
ISSN
0893-6080
Abstract
This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples. (C) 2016 Elsevier Ltd. All rights reserved.
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