Event-triggered quantized L2 - L∞ filtering for neural networks under denial-of-service attacks

作者：Zhou, Youmei; Chang, Xiao-Heng^*

来源：INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2022, 32(10): 5897-5918.

DOI：10.1002/rnc.6121

• denial-of-service attacks
• event-triggeredmechanism
• L-2 - L-infinity filter
• neural networks
• quantization

This article deals with the reliable event-triggered quantized L-2 - L-infinity filtering issue for neural networks with exterior interference under denial-of-service attacks. In order to lighten the load of communication channels and save network resources, a resilient event-triggered mechanism and a quantization scheme are employed, simultaneously. By applying a piecewise Lyapunov-Krasovskii functional method, sufficient conditions containing limitations of denial-of-service attacks are derived to guarantee that the filter error system is exponentially stable as well as possesses a prescribed L-2 - L-infinity disturbance attenuation performance. Then, a co-design method of the desired quantized L-2 - L-infinity filtering gain matrix and event-triggering parameter can be obtained provided that the linear matrix inequalities have a feasible solution. Finally, the usefulness of the proposed design method is demonstrated by a numerical example.