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Fixed-Time Synchronization of Neural Networks with Parameter Uncertainties via Quantized Intermittent Control

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Abstract

In this paper, we study the fixed-time synchronization (FIXTS) of neural networks (NNs) with parameter uncertainties via quantized intermittent control. Based on the intermittent control strategy and quantitative control theory, sufficient conditions are established to achieve synchronization of NNs and the synchronization time can be estimated. In addition, this paper also considers the synchronization of NNs under different situations. Finally, a simulation example is given to verify the correctness of the proposed theory.

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Acknowledgements

This work is supported by Fundamental Research Funds for the Central Universities, China (Project No. SWU020005).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People’s Republic of China

    Wenqiang Yang, Junjian Huang & Xin Wang

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  1. Wenqiang Yang
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  2. Junjian Huang
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  3. Xin Wang
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Correspondence to Junjian Huang.

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Yang, W., Huang, J. & Wang, X. Fixed-Time Synchronization of Neural Networks with Parameter Uncertainties via Quantized Intermittent Control. Neural Process Lett 54, 2303–2318 (2022). https://doi.org/10.1007/s11063-021-10731-3

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