Stability of Complex-Valued Recurrent Neural Networks With Time-Delays

This brief points out two mistakes in a recently published paper on complex-valued recurrent neural networks (RNNs). Moreover, a new condition for the complex-valued activation function is presented, which is less conservative than the Lipschitz condition that is widely assumed in the literature. Based on the new condition and linear matrix inequality, some new criteria to ensure the existence, uniqueness, and globally asymptotical stability of the equilibrium point of complex-valued RNNs with time delays are established. A numerical example is given to illustrate the effectiveness of the theoretical results.