Event-triggered finite-time dissipative control for fractional-order neural networks with uncertainties

In this paper, the focus is on addressing the problems of designing an event-triggered finite-time dissipative control strategy for fractional-order neural networks (FONNs) with uncertainties. Firstly, the Zeno behavior of the fractional-order neural networks model is discussed. Utilizing inequality techniques, we calculate a positive lower bound for inter-execution intervals, which serves to resolve issues related to infinite triggering and sampling. Secondly, we formulate an event-triggered control scheme to solve the finite-time dissipative control problems. Through the application of finite-time boundedness theory, fractional-order calculus properties, and linear matrix inequality techniques, we derive sufficient conditions for the existence of such an event-triggered finite-time dissipative state-feedback control for the considered systems. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methodology.