Non-reduced order method to global h-stability criteria for proportional delay high-order inertial neural networks
作者：
Highlights：
• The problem of global h stability for proportional delay HOINNs is investigated for the first time.
• On the one hand, the h stability characterization generalizes the notions of uniform stability, exponential stability and asymptotically stability. On the other hand, h stability relaxes the concept of stability, including the case where the system trajectory converges to the neighborhood of the origin.
• A new LKF contain ing a function h(t) is constructed.
• The delay dependent global h stabil ity criteria of proportional delay HOINNs also apply to the proportional delay INNs.
摘要
•The problem of global h stability for proportional delay HOINNs is investigated for the first time.•On the one hand, the h stability characterization generalizes the notions of uniform stability, exponential stability and asymptotically stability. On the other hand, h stability relaxes the concept of stability, including the case where the system trajectory converges to the neighborhood of the origin.•A new LKF contain ing a function h(t) is constructed.•The delay dependent global h stabil ity criteria of proportional delay HOINNs also apply to the proportional delay INNs.
论文关键词：High-order inertial neural networks,Proportional delays,Global h-stability,Non-reduced order method
论文评审过程：Received 7 December 2020, Revised 23 March 2021, Accepted 22 April 2021, Available online 11 May 2021, Version of Record 11 May 2021.
论文官网地址：https://doi.org/10.1016/j.amc.2021.126308
