Event-triggered generalized dissipative filtering for delayed neural networks under aperiodic DoS jamming attacks

Abstract

This paper is concerned with event-triggered generalized dissipative filtering for delayed neural networks under aperiodic denial-of-service (DoS) attacks. Note that DoS attacks impede the wireless communications on measurement from time to time. This paper aims at designing resilient filters against DoS attacks to estimate neuronal states in the sense of dissipative. For this goal, a switched filter is introduced to cope with DoS attacks and unavailability of state information. In order to save precious communication resources, an event-triggered communication scheme is devised to transmit only necessary information to the filter. With such setting, the filtering error system is modeled as a switched system with time-delay. By employing the piecewise Lyapunov–Krasovskii functional approach and linear matrix inequality techniques, some criteria on the existence of suitable filters are presented against aperiodic DoS attacks while suitable filtering performance can be ensured. It should be mentioned that since a generalized dissipative performance index is introduced, several kinds of resilient event-based filtering issues, such as H∞ filtering, passive filtering, mixed H∞ and passive filtering, (Q, S, R)-dissipative filtering are solved in a unified framework in the presence of aperiodic DoS attacks. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.