Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks with Time Varying Delays

Complex-valued neural networks (CVNNs) contain complex-valued parameters and variables, which is more effective when dealing with complex signals. In order to extend and complement the known results of CVNNs, in this paper, the problem of finite-time projective synchronization is explored for a class of stochastic complex-valued neural networks (SCVNNs) with time-varying delays. Based on the Lyapunov stability approach and inequalities techniques, some novel projective synchronization criteria are established by decomposing SCVNNs into real and imaginary parts. Finally, a numerical simulation is presented to demonstrate the effectiveness of the proposed control scheme and the obtained theoretical results.