Adaptive finite-time synchronization of stochastic mixed time-varying delayed memristor-based neural networks

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This paper focuses on the finite-time synchronization of stochastic memristor-based neural networks with time-varying discrete and distributed delays and discontinuous nonlinear functions via the adaptive state-feedback controller. Based on the theories of set-valued mappings and stochastic differential inclusions, the finite-time synchronization of the drive neural network and response neural network is transformed into the finite-time stabilization problem of the corresponding error stochastic neural network. By choosing an appropriate Lyapunov function and employing the theory of stochastic finite-time stability, we present a method to design the control gain parameters. Finally, an example verifies the validity of the proposed method.