Global exponential p-norm stability of BAM neural networks with unbounded time-varying delays: A method based on the representation of solutions

Abstract

This paper studies the global exponential [Formula: see text]-norm stability of bidirectional associative memory (BAM) neural networks with unbounded time-varying delays. A novel method based on the representation of solutions is put forward to deduce a global exponential [Formula: see text]-norm stability criterion. This method does not need to set up any Lyapunov–Krasovskii functionals (LKF), which can greatly reduce a large amount of computations and is simpler than the existing methods. In the end, representative numerical examples are given to illustrate the availability of the method.