A Representation of System Solutions for Global Exponential Stabilization of Memristor-Based Neural Networks With Unbounded Time-Varying Delays

Abstract

This article mainly discusses the global exponential stabilization of a class of memristor-based neural networks (MNNs) with unbounded time-varying delays. In this article, a new approach without constructing any Lyapunov–Krasovskii functional is proposed to derive the global exponential stabilization criterion containing only a few simple inequalities. This approach is based on the representation of the system solution, which can derive simpler criteria than the existing approaches. In addition, this approach does not limit the range of the derivative of the unbounded time-varying delay, which implies that the stabilization criterion obtained is weaker conservative. Finally, two numerical examples are employed to demonstrate the effectiveness of the approach.

Keywords

• System solution
• memristor-based neural networks
• global exponential stabilization
• representation
• unbounded time-varying delays