TY - JOUR
T1 - Finite-time synchronization of fractional-order memristive neural networks via feedback and periodically intermittent control
AU - Hui, Meng
AU - Wei, Chen
AU - Zhang, Jiao
AU - Iu, Herbert Ho-Ching
AU - Yao, Rui
AU - Bai, Lin
PY - 2023/1
Y1 - 2023/1
N2 - This paper discusses the finite-time synchronization (FTS) of fractional-order memristive neural networks (FMNNs) with time-varying delays. Firstly, based on Gronwall-Bellman inequality and the fractional-order derivative of the power function, two novel propositions on finite-time fractional functional differential inequality are built. Secondly, the feedback controller and intermittent controller designed in this paper are all delay -independent controllers, which can work even when the prior state cannot be measured or the specific time delay function is unknown. Thirdly, in addition to the traditional Lyapunov function with absolute value form, a more general and flexile Lyapunov function based on p-norm form is constructed to analyze the criteria of FTS. Meanwhile, an improved estimation of settling time of fractional system is given explicitly, which is more accurate and general than the existing results. Eventually, the validity of theoretical analysis is confirmed by numerical examples. (C) 2022 Elsevier B.V. All rights reserved.
AB - This paper discusses the finite-time synchronization (FTS) of fractional-order memristive neural networks (FMNNs) with time-varying delays. Firstly, based on Gronwall-Bellman inequality and the fractional-order derivative of the power function, two novel propositions on finite-time fractional functional differential inequality are built. Secondly, the feedback controller and intermittent controller designed in this paper are all delay -independent controllers, which can work even when the prior state cannot be measured or the specific time delay function is unknown. Thirdly, in addition to the traditional Lyapunov function with absolute value form, a more general and flexile Lyapunov function based on p-norm form is constructed to analyze the criteria of FTS. Meanwhile, an improved estimation of settling time of fractional system is given explicitly, which is more accurate and general than the existing results. Eventually, the validity of theoretical analysis is confirmed by numerical examples. (C) 2022 Elsevier B.V. All rights reserved.
KW - Fractional-order memristive neural
KW - networks
KW - Finite-time synchronization
KW - Estimation of settling time
KW - Delay-independent controller
KW - LAG SYNCHRONIZATION
KW - STABILITY
KW - STABILIZATION
UR - http://www.scopus.com/inward/record.url?scp=85137037472&partnerID=8YFLogxK
U2 - 10.1016/j.cnsns.2022.106822
DO - 10.1016/j.cnsns.2022.106822
M3 - Article
SN - 1007-5704
VL - 116
JO - Communications in Nonlinear Science and Numerical Simulation
JF - Communications in Nonlinear Science and Numerical Simulation
M1 - 106822
ER -