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Quantitative Analysis in Delayed Fractional-Order Neural Networks

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Abstract

This paper mainly investigates the influence of self-connection delay on bifurcation in a fractional neural network. The bifurcation criteria for the proposed systems with self-connection delay or without self-connection delay is figured out using time delay as a bifurcation parameter, respectively. The effects of self-connection delay on bifurcation in a fractional neural network are ascertained in this paper. Comparative analysis indicates that the stability performance of the proposed fractional neural networks is overly undermined by self-connection delay, which cannot be disregarded. In addition, the impact of fractional order on the bifurcation point is revealed. To highlight the proposed original results, two numerical examples are finally presented.

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Authors and Affiliations

  1. School of Information Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China

    Jun Yuan

  2. School of Mathematics and Statistics, Xinyang Nomal University, Xinyang, 464000, China

    Chengdai Huang

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  1. Jun Yuan
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  2. Chengdai Huang
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Correspondence to Jun Yuan.

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Yuan, J., Huang, C. Quantitative Analysis in Delayed Fractional-Order Neural Networks. Neural Process Lett 51, 1631–1651 (2020). https://doi.org/10.1007/s11063-019-10161-2

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  • DOI: https://doi.org/10.1007/s11063-019-10161-2

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