Event‐triggered quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering for neural networks under denial‐of‐service attacks

• Published in: International journal of robust and nonlinear control Vol. 32; no. 10; pp. 5897 - 5918
• Main Authors: Zhou, Youmei, Chang, Xiao‐Heng
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 10.07.2022
• Subjects: denial‐of‐service attacks; event‐triggered mechanism; Filtration; L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filter; Linear matrix inequalities; Neural networks; quantization
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.6121

This article deals with the reliable event‐triggered quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ 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 L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ disturbance attenuation performance. Then, a co‐design method of the desired quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ 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.