Existence, uniqueness, and exponential stability analysis for complex-valued memristor-based BAM neural networks with time delays

This article explores the exponential stability problem of complex-valued bidirectional associative memory (BAM) neural networks with time delays. This analysis is on the basis of the M-matrix approach, the differential inclusions theory and the homeomorphism property. By constructing a novel Lyapunov functional, a sufficient criterion for the existence, uniqueness, and exponential stability for the equilibrium point of the considered system is derived. Moreover, similar results in terms of M-matrix are also obtained for the exponential stability problem of delayed complex-valued BAM neural networks without memristors. In the end, two numerical examples are provided to demonstrate the availability of the obtained results. (C) 2017 Elsevier Inc. All rights reserved.