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Robust Stabilization of Delayed Neural Networks: Dissipativity-Learning Approach

Authors
Saravanakumar, RamasamyKang, Hyung SooAlm, Choon KiSu, XiaojieKarimi, 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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