Robust Synchronization of Complex-Valued Neural Networks with Probabilistic Delay

Abstract

This paper focuses on the hybrid effects of parameter uncertainties, stochastic perturbation, leakage delay and probabilistic time-varying delay on the synchronization of complex-valued neural networks. Based on the stochastic analysis and Lyapunov theory, sufficient conditions are derived to ensure the networks under consideration to be globally robustly synchronized in the mean square. In addition, an example is also given to illustrate the effectiveness of the obtained theoretical results.