Finite-time stabilization of memristor-based inertial neural networks with time-varying delays combined with interval matrix method
作者：
Highlights：
• The MINNs are converted to a type of system with interval parameters by the approach of convex combination of matrices, which handles the problem of mismatch between the parameter switching behavior of the memristor.
• Two types of controllers with time-varying delays are designed by the definition of stabilization in finite time, which deals with time-varying delays in different ways.
• The challenge of finite-time stabilization of MINNs is investigated with the interval matrix approach, and some new results in form of LMIs are obtained.
摘要
•The MINNs are converted to a type of system with interval parameters by the approach of convex combination of matrices, which handles the problem of mismatch between the parameter switching behavior of the memristor.•Two types of controllers with time-varying delays are designed by the definition of stabilization in finite time, which deals with time-varying delays in different ways.•The challenge of finite-time stabilization of MINNs is investigated with the interval matrix approach, and some new results in form of LMIs are obtained.
论文关键词：Memristor-based inertial neural networks,Finite-time stabilization,Interval matrix methods,Feedback controllers
论文评审过程：Received 8 March 2021, Revised 25 May 2021, Accepted 10 August 2021, Available online 18 August 2021, Version of Record 26 August 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107395
