Further results on L2-L∞ state estimation of delayed neural networks

This paper investigates the L2-L∞ state estimation problem for a class of delayed neural networks. Attention is focused on the design of a full-order state estimator such that the prescribed L2-L∞ performance constraint can be ensured. By utilizing the time-delay information sufficiently, a novel L2-L∞ performance analysis approach is proposed in this paper for the first time. Based on such an approach, the less conservative sufficient conditions are established in terms of linear matrix inequalities under which the L2-L∞ performance level can be achieved for the estimation error dynamics. Several numerical examples show that the proposed approach in this paper is explicitly effective in reducing the possible conservatism.