Open AccessExponential synchronization control of delayed memristive neural network based on canonical Bessel-Legendre inequality
Author(s) -
Xingxing Song,
AUTHOR_ID,
Pengfei Zhi,
Wanlu Zhu,
Hui Wang,
Haiyang Qiu
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2022262
Subject(s) - bessel function , mathematics , synchronization (alternating current) , control theory (sociology) , controller (irrigation) , exponential stability , artificial neural network , legendre polynomials , exponential function , control (management) , computer science , topology (electrical circuits) , mathematical analysis , nonlinear system , combinatorics , physics , artificial intelligence , quantum mechanics , machine learning , agronomy , biology
In this paper, we study the exponential synchronization problem of a class of delayed memristive neural networks(MNNs). Firstly, a intermittent control scheme is designed to solve the parameter mismatch problem of MNNs. A discontinuous controller with two tunable scalars is designed, and the upper limit of control gain can be adjusted flexibly. Secondly, an augmented Lyaponov-Krasovskii functional(LKF) is proposed, and vector information of N-order canonical Bessel-Legendre(B-L) inequalities is introduced. LKF method is used to obtain the stability criterion to ensure exponential synchronization of the system. The conservatism of the result decreases with the increase of the order of the B-L inequality. Finally, the effectiveness of the main results is verified by two simulation examples.