Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals
- 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
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.