Resilient Event-triggered H∞ Filtering for Delayed Neural Networks Under Nonperiodic DoS Jamming Attacks

This paper is concerned with event-triggered H_∞ filtering for delayed neural network (NN) under nonperiodic denial-of-service (DoS) attacks, which aim at impeding the wireless communications on measurement from time to time. Firstly, to adapt to the DoS attacks and unavailability of state information, a piecewise filter is introduced. Secondly, in order to save precious communication resources, a resilient filter based on event-triggered communication scheme is designed under the nonperiodic DoS jamming attacks. Thirdly, using time-delay modelling and switching system modeling methods, the piecewise augmented system model with time delay is established. Based on this model, a suitable filter is designed such that certain filtering performance can be ensured, by using piecewise Lyapunov-Krasovskii functional approach and linear matrix inequalities (LMI) technique. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.