Journal
MATHEMATICS
Volume 10, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/math10101725
Keywords
complex-valued inertial neural networks; state estimation; multiple time delays
Categories
Funding
- National Natural Science Foundation of China [62173214]
- Natural Science Foundation of Shandong Province of China [ZR2021MF100]
- Research Fund for the Taishan Scholar Project of Shandong Province of China
- Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China [2019KJI005]
- SDUST Research Fund
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This paper considers the problem of state estimation for complex-valued inertial neural networks with leakage, additive and distributed delays. A delay-dependent criterion based on linear matrix inequalities (LMIs) is derived using the Lyapunov-Krasovskii functional method, the Jensen inequality, and the reciprocally convex approach. The network state is estimated by observing the output measurements to ensure global asymptotic stability of the error system. Two examples are provided to verify the effectiveness of the proposed method.
In this paper, the problem of state estimation for complex-valued inertial neural networks with leakage, additive and distributed delays is considered. By means of the Lyapunov-Krasovskii functional method, the Jensen inequality, and the reciprocally convex approach, a delay-dependent criterion based on linear matrix inequalities (LMIs) is derived. At the same time, the network state is estimated by observing the output measurements to ensure the global asymptotic stability of the error system. Finally, two examples are given to verify the effectiveness of the proposed method.
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