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Cycle sampling neural network algorithms and applications

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Abstract

Two improved sampling neural network (SNN) algorithms, Cycle SNN (CSNN) and Rolling-Cycle SNN (RSNN), are proposed and optimized in this study, to improve the accuracy of basic SNN (BSNN). Experiments show that the improved algorithms achieve significant improvements in both accuracy and training efficiency. This study also perfects the SNN theoretical system and unifies the vector form of these SNN algorithms. Based on the theoretical analysis, this can be achieved by effectively reducing the high-frequency component and aliasing distortion through cycle extension and rolling. These efforts have made useful contributions to explore the potential and prospects of SNN applications. The SNN networks with a new structure and SNN error diffusion (SNN-ED) convergence method provide a new idea for the development of neural networks.

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Data Availability

The data of NTC thermo-sensitive semiconductor resistors were derived from the following resources available in the public domain: https://www.sinochip.net/ [45].

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Acknowledgements

This work was supported by the Science and Technology Research Foundation of the Education Department in Jiangxi Province (No. GJJ218503, GJJ219105).

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Author notes

  1. Lingyan Wu contributed equally to this work and should be considered co-first author.

Authors and Affiliations

  1. Department of Mathematics and Information Science, Science and Technology College Gannan Normal University, 61 Hongqi Street, Ganzhou, 341000, Jiangxi, China

    Gang Cai

  2. Faculty of Intelligent Manufacturing and Automotive Engineering, Gannan University of Science and Technology, 156 Kejia Street, Ganzhou, 341000, Jiangxi, China

    Lingyan Wu

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  1. Gang Cai
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Correspondence to Lingyan Wu.

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Cai, G., Wu, L. Cycle sampling neural network algorithms and applications. J Supercomput 79, 9889–9914 (2023). https://doi.org/10.1007/s11227-022-05019-9

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