Event-triggered H∞ filtering for delayed neural networks via sampled-data

This paper is concerned with event-triggered H-infinity filtering for delayed neural networks via sampled data. A novel event-triggered scheme is proposed, which can lead to a significant reduction of the information communication burden in the network; the feature of this scheme is that whether or not the sampled data should be transmitted is determined by the current sampled data and the error between the current sampled data and the latest transmitted data. By constructing a proper Lyapunov-Krasovskii functional, utilizing the reciprocally convex combination technique and Jensen's inequality sufficient conditions are derived to ensure that the resultant filtering error system is asymptotically stable. Based on the derived H-infinity performance analysis results, the H-infinity filter design is formulated in terms of Linear Matrix Inequalities (LMIs). Finally, the proposed stability conditions are demonstrated with numerical example. (C) 2017 Elsevier Ltd. All rights reserved.