Global polynomial stabilization and global asymptotic stabilization of coupled neural networks with multi‐proportional delays
Rui Zhou, Liqun Zhou · Mathematical Methods in the Applied Sciences · 2020
This paper first puts forward the concept of global polynomial stabilization (GPS) of coupled neural networks (CNNs) with multi‐proportional delays. First, the existence and uniqueness of the equilibrium point of the proposed CNNs are proved by using homeomorphic mapping theorem. Second, by taking discrete controller, based on Lyapunov functionals and linear matrix inequality (LMI) skills, we gain several delay‐dependent GPS and global asymptotic stabilization (GAS) criteria for the proposed system, and these criteria can be validated by Matlab. The relationship among GPS, global exponential stabilization (GES), and GAS is also revealed. In the end, our results are confirmed by examples.