Finite-time synchronization of stochastic memristor-based delayed neural networks

The finite-time synchronization problem of stochastic memristor-based delayed neural network is studied. Certain sufficient conditions are got to assure finite-time synchronization of the chaotic stochastic memristor-based neural networks by using differential inclusions theory, finite-time stability theorem, Lyapunov functional, inequality techniques, stochastic analysis theory and designing a suitable controller. Comparison with previous results, the model of memristor-based neural network of this paper is general, and the given stability conditions are novel. Therefore, the obtained results generalize and improve some existing achievements about the memristor-based neural network. Moreover, a numerical simulation example demonstrates the usefulness of the theoretical results.