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New approach to finite-time stability for fractional-order BAM neural networks with discrete and distributed delays

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  • Du, Feifei
  • Lu, Jun-Guo

Abstract

The finite-time stability (FTS) for fractional-order bidirectional associative memory neural networks (FOBAMNNs) with discrete and distributed delays is investigated in this article. Firstly, a novel fractional-order delayed Gronwall inequality (FODGI) with two different orders is developed. As a straightforward generalization, a novel generalized FODGI with multiple different orders is also established, which can be applied to investigate the stability of fractional-order delayed systems (FODSs) with multiple different orders. Secondly, based on the former inequality, two novel FTS criteria of FOBAMNNs with the orders 0<℘<1 and 1<℘<2 are established. Finally, three examples are exhibited to illustrate the effectiveness and the less conservativeness of the proposed methods.

Suggested Citation

  • Du, Feifei & Lu, Jun-Guo, 2021. "New approach to finite-time stability for fractional-order BAM neural networks with discrete and distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:chsofr:v:151:y:2021:i:c:s0960077921005798
    DOI: 10.1016/j.chaos.2021.111225
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    References listed on IDEAS

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    1. Zhanying Yang & Jie Zhang, 2019. "Stability Analysis of Fractional-Order Bidirectional Associative Memory Neural Networks with Mixed Time-Varying Delays," Complexity, Hindawi, vol. 2019, pages 1-22, October.
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    5. Gao, Fei & Li, Xiling & Li, Wenqin & Zhou, Xianjin, 2021. "Stability analysis of a fractional-order novel hepatitis B virus model with immune delay based on Caputo-Fabrizio derivative," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
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    8. Nabi, Khondoker Nazmoon & Abboubakar, Hamadjam & Kumar, Pushpendra, 2020. "Forecasting of COVID-19 pandemic: From integer derivatives to fractional derivatives," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
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