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

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• 作者：G. Rajchakit^a ; R. Saravanakumar^a ; Choon Ki Ahn^b ; ^{hironaka@korea.ac.kr} ; Hamid Reza Karimi^c
• 关键词：Generalized neural network ; Stability analysis ; Time-varying delay ; Weighted integral inequality
• 刊名：Neural Networks
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：86
• 期：Complete
• 页码：10-17
• 全文大小：554 K
• 卷排序：86

文摘

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.