A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen–Grossberg neural networks
作者：
Highlights：
• This paper focuses on the problem of global exponential stability analysis for high-order delayed discrete-time Cohen-Grossberg neural networks. We present a new method to obtain the GES criteria of delayed discrete-time HOCGNNs, which does not need to construct any LKF or auxiliary function.
• Compared with the existing results, the GES criteria obtained by this method involve less decision variables, which means that these stability criteria have lower computational complexity.
• A new equivalent condition of nonsingular M-matrix is investigated, which is convenient to check the obtained stability criteria.
摘要
•This paper focuses on the problem of global exponential stability analysis for high-order delayed discrete-time Cohen-Grossberg neural networks. We present a new method to obtain the GES criteria of delayed discrete-time HOCGNNs, which does not need to construct any LKF or auxiliary function.•Compared with the existing results, the GES criteria obtained by this method involve less decision variables, which means that these stability criteria have lower computational complexity.•A new equivalent condition of nonsingular M-matrix is investigated, which is convenient to check the obtained stability criteria.
论文关键词：High-order neural networks,Global exponential stability,Nonsingular M-matrix,Multiple time-varying delays
论文评审过程：Received 17 October 2019, Revised 5 April 2020, Accepted 24 May 2020, Available online 11 June 2020, Version of Record 11 June 2020.
论文官网地址：https://doi.org/10.1016/j.amc.2020.125401
