Adaptive synchronization for stochastic competitive neural networks with mixed time-varying delays

This paper deals with the synchronization problem for competitive neural networks with different time scales, as well as mixed time-varying delays (both discrete and distributed time-varying delays) and stochastic disturbance. By using stochastic analysis approaches and constructing a novel Lyapunov–Krasovskii functional, an adaptive feedback controller is proposed to guarantee the exponential synchronization of proposed competitive neural networks in terms of p-norm. The synchronization results presented in this paper generalize and improve many known results. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the theoretical results.

► Synchronization for competitive neural networks in terms of p-norm is studied. ► Adaptive control and stochastic analysis are employed to derive new conditions. ► The derived results generalize and improve many known results. ► A numerical example is given to show the effectiveness of the main results.