Robust Stabilization of Delayed Neural Networks: Dissipativity-Learning Approach
© 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 netw...
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| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/50642 |
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| Institution: | Mahidol University |
| Summary: | © 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. |
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