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Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays |
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| Authors: | Saravanakumar R. Rajchakit Grienggrai Ali M. Syed Xiang Zhengrong Joo Young Hoon |
| Affiliation: | 1.Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai, 50290, Thailand ;2.Department of Mathematics, Thiruvalluvar University, Vellore, 632115, Tamil Nadu, India ;3.School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China ;4.Department of Control and Robotics Engineering, Kunsan National University, Kunsan, Chonbuk, 573-701, Republic of Korea ;5.Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Thung Khru, Bangkok, 10140, Thailand ; |
| Abstract: | In this draft, we consider the problem of robust extended dissipativity for uncertain discrete-time neural networks (DNNs) with time-varying delays. By constructing appropriate Lyapunov–Krasovskii functional (LKF), sufficient conditions are established to ensure that the considered time-delayed uncertain DNN is extended dissipative. The derived conditions are presented in terms of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the superiority of this result. |
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