Event‐triggered quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering for neural networks under denial‐of‐service attacks
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 res...
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Published in | International journal of robust and nonlinear control Vol. 32; no. 10; pp. 5897 - 5918 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Bognor Regis
Wiley Subscription Services, Inc
10.07.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 1049-8923 1099-1239 |
DOI: | 10.1002/rnc.6121 |