Yufeng Zhou

Area of research

Computer Networks and Communications · Statistical and Nonlinear Physics

Research interest

Research topics from publications: Event-triggered impulsive control on quasi-synchronization of memristive neural networks with time-varying delays; Quasi-Synchronization of Delayed Memristive Neural Networks via a Hybrid Impulsive Control; Synchronization of memristive neural networks with unknown parameters via event-triggered adaptive control; Event-Triggered Finite-Time Stabilization of Fuzzy Neural Networks With Infinite Time Delays and Discontinuous Activations. Representative work: This paper investigates the quasi-synchronization of delayed memristive neural networks (MNNs) via a novel hybrid impulsive control algorithm which combines time-triggered and event-triggered impulsive control. The relationship between a predesigned non-negative auxiliary function and a given exponentially decreasing threshold function is used to describe the switching. Under this novel controller, sufficient conditions for the quasi-synchronization are derived by the impulsive differential inequality. In addition, by choosing appropriate parameters or initial conditions such that the initial value of the non-negative auxiliary function is less than that of the event-triggered function, the This article unifies the stability criteria of asymptotic, exponential, and finite-time control within a single framework for fuzzy neural networks (FNNs) with infinite time delays. First, the boundedness and differentiability for time delays are removed. Then, Lipschitz condition for activation function is relaxed, which is allowed to have jumping discontinuous points. To stabilize FNNs, the analytical method is established by comparison principle, contradiction method and inequality techniques. Moreover, different from the traditional Lyapunov method and finite-time stability theorem, several sufficient conditions are deduced and the suppression functions are designed to guarantee asymptot

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