@article{Li2023, 
author = {Biwen Li and Yibo Sun},
title = {Stability analysis of Cohen-Grossberg neural networks with time-varying delay by flexible terminal interpolation method},
year = {2023},
journal = {AIMS Mathematics},
volume = {8},
number = {8},
pages = {17744-17764},
keywords = {switched system, Cohen-Grossberg neural networks, flexible terminal interpolation method, linear matrix inequalities, stability analysis},
url = {https://www.sciopen.com/article/10.3934/math.2023906},
doi = {10.3934/math.2023906},
abstract = {In the paper, the existence and uniqueness of the equilibrium point in the Cohen-Grossberg neural network (CGNN) are first studied. Additionally, a switched Cohen-Grossberg neural network (SCGNN) model with time-varying delay is established by introducing a switched system to the CGNN. Based on reducing the conservativeness of the system, a flexible terminal interpolation method is proposed. Using an adjustable parameter to divide the invariant time-delay interval into multiple adjustable terminal interpolation intervals    (      2          ı      +      1        −  3  ), more moments when signals are transmitted slowly can be captured. To this end, a new Lyapunov-Krasovskii functional (LKF) is constructed, and the stability of SCGNN can be estimated. Using the LKF method, a quadratic convex inequality, linear matrix inequalities (LMIs) and ordinary differential equation theory, a new form of stability criterion is obtained and specific instances are given to prove the applicability of the new stability criterion.}
}