Global exponential stability of uncertain memristor-based recurrent neural networks with mixed time delays

The global exponential stability of the equilibrium point for uncertain memristor-based recurrent neural networks is studied in this paper. The memristor-based recurrent neural networks considered in this paper are based on a realistic memristor model, and can be considered as the extension of some existing memristor-based recurrent neural networks. By virtue of homomorphic theory, it is proved that the uncertain memristor-based recurrent neural networks have a unique equilibrium point under some mild assumptions. Moreover, the unique equilibrium point is proved to be globally exponentially stable by constructing a suitable Lyapunov functional. Finally, the obtained results are applied to determine the dynamical behaviors and circuit design of the memristor-based recurrent neural networks by some numerical examples.