Publication: Robust Stabilization of Delayed Neural Networks: Dissipativity-Learning Approach
Issued Date
2019-03-01
Resource Type
ISSN
21622388
2162237X
2162237X
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2-s2.0-85050997468
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Mahidol University
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SCOPUS
Bibliographic Citation
IEEE Transactions on Neural Networks and Learning Systems. Vol.30, No.3 (2019), 913-922
Suggested Citation
Ramasamy Saravanakumar, Hyung Soo Kang, Choon Ki Ahn, Xiaojie Su, Hamid Reza Karimi Robust Stabilization of Delayed Neural Networks: Dissipativity-Learning Approach. IEEE Transactions on Neural Networks and Learning Systems. Vol.30, No.3 (2019), 913-922. doi:10.1109/TNNLS.2018.2852807 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/50642
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Title
Robust Stabilization of Delayed Neural Networks: Dissipativity-Learning Approach
Abstract
© 2012 IEEE. This paper examines the robust stabilization problem of continuous-time delayed neural networks via the dissipativity-learning approach. A new learning algorithm is established to guarantee the asymptotic stability as well as the (Q,S,R) - α -dissipativity of the considered neural networks. The developed result encompasses some existing results, such as H ∞ and passivity performances, in a unified framework. With the introduction of a Lyapunov-Krasovskii functional together with the Legendre polynomial, a novel delay-dependent linear matrix inequality (LMI) condition and a learning algorithm for robust stabilization are presented. Demonstrative examples are given to show the usefulness of the established learning algorithm.