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Event-Triggered Reliable Dissipative Filtering for Delayed Neural Networks with Quantization

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Abstract

This paper investigates the event-triggered reliable dissipative filtering for delayed neural networks with quantization. First, an event-triggered scheme is introduced to save limited network resources, by which whether or not sampled signals should be transmitted to the quantizer depends on a predefined event-triggered condition. Second, with the event-triggered scheme, a new unified sampled-data filtering error system is established to deal with the issue of dissipative filtering for the neural networks with quantization. Third, by using the Lyapunov–Krasovskii functional method, a sufficient criterion is obtained to ensure asymptotic stability and strict \(({\mathscr {Q}},{\mathscr {S}},{\mathscr {R}})\)-\(\alpha \)-dissipativity for the filtering error system. Then, based on solutions to a set of linear matrix inequalities, both proper event-triggered parameters and filter parameters can be co-designed. Finally, the effectiveness and the superiority of the proposed method are verified by numerical simulation via two examples.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61741308, 61703153), the Natural Science Foundation of Hunan Province (Grant Nos. 2018JJ4075, 2020JJ2013), and the Scientific Research Fund of Hunan Provincial Education Department (Grant Nos. 19B149, 19C0582).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, 412007, China

    Gang Chen, Yun Chen, Wei Wang, Yaqi Li & Hongbing Zeng

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  1. Gang Chen
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Correspondence to Wei Wang.

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Chen, G., Chen, Y., Wang, W. et al. Event-Triggered Reliable Dissipative Filtering for Delayed Neural Networks with Quantization. Circuits Syst Signal Process 40, 648–668 (2021). https://doi.org/10.1007/s00034-020-01509-4

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