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Finite-Time Dissipative Synchronization for Markovian Jump Generalized Inertial Neural Networks With Reaction-Diffusion Terms

Authors
Song, XiaonaMan, JingtaoAhn, Choon KiSong, Shuai
Issue Date
6월-2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Synchronization; Artificial neural networks; Delays; Delay effects; Time-varying systems; Biological neural networks; Markov processes; Finite-time dissipative synchronization; generalized inertial neural networks (GINNs); Markovian jump parameters; reaction& #8211; diffusion terms; time-varying memory-based controller
Citation
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, v.51, no.6, pp.3650 - 3661
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume
51
Number
6
Start Page
3650
End Page
3661
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/127936
DOI
10.1109/TSMC.2019.2958419
ISSN
2168-2216
Abstract
A novel generalized neural network (NN), which includes Markovian jump parameters, inertial items, and reaction-diffusion terms, is proposed, and the issue of finite-time dissipative synchronization for this kind of NNs is discussed in this article. First, an appropriate variable substitution is employed so that the original second-order differential system is transformed into a first-order one. Second, a novel time-varying memory-based controller is designed to ensure the dissipative synchronization of the drive and response systems over a finite-time interval. Then, a new Lyapunov-Krasovskii function is processed by reciprocally convex combination and free-weighting matrix methods, therefore, a less conservative synchronization criterion is derived. Finally, by providing three examples, the feasibility, superiority, and practicality of the obtained results are illustrated.
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공과대학 (전기전자공학부)
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