Stochastic Exponential Robust Stability of a Class of Complex-Valued Neural Networks

In order to analyze the influence of the Markova jumping parameters on the system, this paper deals with dynamic behavior analysis for a class of interval neural networks defined in complex number domain with Markova jumping parameters and time-varying delays. It is assumed that the activation functions defined in complex number domain satisfy Lipschitz condition. Firstly, the existence and uniqueness of the equilibrium point of the addressed system are studied by employing the M-matrix theory and the homeomorphism mapping theory. Then, the stochastic exponential robust stability of the equilibrium point is analyzed based on the idea of the vector Lyapunov function method. The presented stability analysis is the generalization of existing ones not only, but also easy to be verified in the practice applications. Finally, a numerical example with several simulation results is given to illustrate the feasibility of the obtained results in this paper.