Finite-Time Dissipative Synchronization for Markovian Jump Generalized Inertial Neural Networks With Reaction-Diffusion Terms
- Authors
- Song, Xiaona; Man, Jingtao; Ahn, Choon Ki; Song, 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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