Event-triggered quantized \(\mathfrak{L}_2-\mathfrak{L}_{\infty}\) filtering for neural networks under denial-of-service attacks. (English) Zbl 1527.93308
Summary: This article deals with the reliable event-triggered quantized \(\mathfrak{L}_2-\mathfrak{L}_\infty\) 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 \(\mathfrak{L}_2-\mathfrak{L}_\infty\) disturbance attenuation performance. Then, a co-design method of the desired quantized \(\mathfrak{L}_2-\mathfrak{L}_\infty\) 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.
{© 2022 John Wiley & Sons Ltd.}
{© 2022 John Wiley & Sons Ltd.}
MSC:
| 93C65 | Discrete event control/observation systems |
| 93E11 | Filtering in stochastic control theory |
| 93B70 | Networked control |
| 93D23 | Exponential stability |
Keywords:
denial-of-service attacks; event-triggered mechanism; \(\mathfrak{L}_2-\mathfrak{L}_\infty\) filter; neural networks; quantizationReferences:
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