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Extended dissipativity of generalised neural networks including time delays |
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| Authors: | R Saravanakumar Grienggrai Rajchakit M Syed Ali |
| Affiliation: | 1. Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai, Thailand;2. Department of Mathematics, Facuty of Science, King Mongkut's University of Technology Thonburi, Bangkok, Thailand;3. Department of Control and Robotics Engineering, Kunsan National University, Kunsan, Chonbuk, Republic of Korea;4. Department of Mathematics, Thiruvalluvar University, Vellore, India |
| Abstract: | 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. |
| Keywords: | Generalised neural network extended dissipativity analysis time-varying delay weighted integral inequality |