TY - JOUR
T1 - Gain-Scheduled Finite-Time Synchronization for Reaction-Diffusion Memristive Neural Networks Subject to Inconsistent Markov Chains
AU - Song, Xiaona
AU - Man, Jingtao
AU - Song, Shuai
AU - Ahn, Choon Ki
N1 - Funding Information:
Manuscript received November 26, 2019; revised April 27, 2020; accepted July 9, 2020. Date of publication July 31, 2020; date of current version July 7, 2021. The work of Xiaona Song was supported in part by the National Natural Science Foundation of China under Grant 61976081 and Grant U1604146 and in part by the Foundation for the University Technological Innovative Talents of Henan Province under Grant 18HASTIT019. The work of Choon Ki Ahn was supported by the National Research Foundation of Korea (NRF) Grant funded by the Ministry of Science and ICT, Korea Government, under Grant NRF-2020R1A2C1005449. (Corresponding author: Choon Ki Ahn.) Xiaona Song and Jingtao Man are with the School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China (e-mail: xiaona_97@163.com; mjt546@163.com).
Publisher Copyright:
© 2012 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - An innovative class of drive-response systems that are composed of Markovian reaction-diffusion memristive neural networks, where the drive and response systems follow inconsistent Markov chains, is proposed in this article. For this kind of nonlinear parameter-varying systems, a suitable gain-scheduled controller that involves a mode and memristor-dependent item is designed, so that the error system is bounded within a finite-time interval. Moreover, by constructing a novel Lyapunov-Krasovskii functional and employing the canonical Bessel-Legendre inequality and free-weighting matrix method, the conservatism of the finite-time synchronization criterion can be greatly reduced. Finally, two numerical examples are provided to illustrate the feasibility and practicability of the obtained results.
AB - An innovative class of drive-response systems that are composed of Markovian reaction-diffusion memristive neural networks, where the drive and response systems follow inconsistent Markov chains, is proposed in this article. For this kind of nonlinear parameter-varying systems, a suitable gain-scheduled controller that involves a mode and memristor-dependent item is designed, so that the error system is bounded within a finite-time interval. Moreover, by constructing a novel Lyapunov-Krasovskii functional and employing the canonical Bessel-Legendre inequality and free-weighting matrix method, the conservatism of the finite-time synchronization criterion can be greatly reduced. Finally, two numerical examples are provided to illustrate the feasibility and practicability of the obtained results.
KW - Canonical Bessel-Legendre (B-L) inequality
KW - finite-time synchronization
KW - gain-scheduled controller
KW - inconsistent Markov chains
KW - Markovian reaction-diffusion memristive neural networks (MNNs)
UR - http://www.scopus.com/inward/record.url?scp=85093660572&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2020.3009081
DO - 10.1109/TNNLS.2020.3009081
M3 - Article
C2 - 32735537
AN - SCOPUS:85093660572
VL - 32
SP - 2952
EP - 2964
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 7
M1 - 9153952
ER -