Exponential stability analysis for delayed complex-valued memristor-based recurrent neural networks

The exponential stability problem for complex-valued memristor-based recurrent neural networks (CVMRNNs) with time delays is studied in this paper. As an extension of real-valued memristor-based recurrent neural networks, CVMRNNs can be separated into real and imaginary parts and an equivalent real-valued system is formed. By constructing a novel Lyapunov function, a new sufficient condition to guarantee the existence, uniqueness, and global exponential stability of the equilibrium point for complex-valued systems is given in terms of M-matrix. The effectiveness of the theoretical result is shown by two numerical examples.