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Extended \(H_{\infty }\) Synchronization Control for Switched Neural Networks with Multi Quantization Densities Based on a Persistent Dwell-Time Approach

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Abstract

This paper thoroughly investigates the synchronization control issue for the switched neural networks. The more comprehensive comparatively switching rule, persistent dwell-time, is applied to actuate the aforementioned neural networks. For tackling the problem caused by the transmission of tremendous data, the quantizer is utilized. The objective is to establish the mixed controller with multi quantization densities for the synchronization error neural networks to meet the various accuracy requirements of the transmitted data. Whereafter, the sufficient conditions of the extended \(H_{\infty }\) performance and global uniform exponential stability for the synchronization error neural networks are constructed. Conclusively, the capability of the proposed mixed controller is elucidated through a numerical example.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61304066, 61573008, 61473178, 61703004, the Natural Science Foundation of Anhui Province under Grant 1708085MF165.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, 243002, People’s Republic of China

    Zhengguo Huang, Hao Shen & Jian Wang

  2. School of Mathematical Sciences, Liaocheng University, Liaocheng, 252059, Shandong, People’s Republic of China

    Jianwei Xia

  3. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, People’s Republic of China

    Xia Huang

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  1. Zhengguo Huang
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Correspondence to Xia Huang.

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Huang, Z., Shen, H., Xia, J. et al. Extended \(H_{\infty }\) Synchronization Control for Switched Neural Networks with Multi Quantization Densities Based on a Persistent Dwell-Time Approach. Neural Process Lett 50, 2821–2841 (2019). https://doi.org/10.1007/s11063-019-10064-2

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