An Exponential Stability Analysis of Complex-Valued Delayed BAM Neural Networks

The objective of this work is to investigate the global exponential stability of delayed complex-valued BAM neural networks without resorting to the standard stability criterion of Lyapunov functions. We derive the sufficient conditions that ensure the exponential stability of the steady state for the addressed neural networks, based on the upper right Dini derivative and Halanay inequality. After separating the non-linear complex-valued function into its real and imaginary parts, the matrix measure approach yields a new set of adequate conditions for the stability of considered systems. Subsequently, a numerical example is shown to illustrate how well the resulting theoretical results work.