区间(图论)
控制理论(社会学)
人工神经网络
数学
正多边形
凸组合
计算机科学
应用数学
凸优化
控制(管理)
人工智能
几何学
组合数学
作者
Ramasamy Saravanakumar,Grienggrai Rajchakit,M. Syed Ali,Young Hoon Joo
标识
DOI:10.1080/00207721.2017.1316882
摘要
This article explores the extended dissipativity conditions for generalised neural networks (GNNs) including interval time-varying delays. Extended dissipativity criterions are proposed by making proper Lyapunov–Krasovskii functional. The improved reciprocally convex combination and weighted integral inequality techniques are together applied in main results to establish the new extended dissipativity conditions of delayed GNNs. Finally, the feasibility and superiority of the proposed novel approach is clearly shown by numerical examples.
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