Non-fragile <i>l</i><sub>2</sub>-<i>l</i><sub>∞</sub> state estimation for time-delayed artificial neural networks: an adaptive event-triggered approach
Licheng Wang, Shuai Liu, Yuhan Zhang, Derui Ding, Xiaojian Yi
Abstract
In this paper, the state estimation problem is investigated for a kind of time-delayed artificial neural networks subject to gain perturbations under the adaptive event-triggering scheme. To avoid wasting resources, the event-triggering scheme is adopted during the data transmission process from the sensors to the estimator where the triggering threshold can be dynamically adjusted. By means of the Lyapunov stability theory, sufficient conditions are provided to ensure that the estimation error dynamics achieves both the asymptotical stability and the l2-l∞ performance. The desired non-fragile estimator gain is parameterised by solving certain matrix inequalities. At last, the usefulness of the proposed event-based non-fragile state estimator is shown via a numerical simulation example.