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Get Free AccessThis 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.
Grienggrai Rajchakit, Ramasamy Saravanakumar, Choon Ki Ahn, Hamid Reza Karimi (2016). Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals. Neural Networks, 86, pp. 10-17, DOI: 10.1016/j.neunet.2016.10.009.
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Type
Article
Year
2016
Authors
4
Datasets
0
Total Files
0
Language
English
Journal
Neural Networks
DOI
10.1016/j.neunet.2016.10.009
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