Robust Stabilization of Delayed Neural Networks: Dissipativity-Learning Approach
- Authors
- Saravanakumar, Ramasamy; Kang, Hyung Soo; Alm, Choon Ki; Su, Xiaojie; Karimi, Hamid Reza
- Issue Date
- 3월-2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Dissipativity learning; Legendre polynomial; neural networks; robust stabilization
- Citation
- IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.30, no.3, pp.913 - 922
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
- Volume
- 30
- Number
- 3
- Start Page
- 913
- End Page
- 922
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/67168
- DOI
- 10.1109/TNNLS.2018.2852807
- ISSN
- 2162-237X
- Abstract
- 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)-alpha-dissipativity of the considered neural networks. The developed result encompasses some existing results, such as H-infinity 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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