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Finite-Time Stability of Fractional-Order BAM Neural Networks with Distributed Delay

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  • Yuping Cao
  • Chuanzhi Bai

Abstract

Based on the theory of fractional calculus, the generalized Gronwall inequality and estimates of mittag-Leffer functions, the finite-time stability of Caputo fractional-order BAM neural networks with distributed delay is investigated in this paper. An illustrative example is also given to demonstrate the effectiveness of the obtained result.

Suggested Citation

  • Yuping Cao & Chuanzhi Bai, 2014. "Finite-Time Stability of Fractional-Order BAM Neural Networks with Distributed Delay," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-8, April.
    • Handle: RePEc:hin:jnlaaa:634803
    DOI: 10.1155/2014/634803
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    Cited by:

    1. Syed Ali, M. & Narayanan, Govindasamy & Shekher, Vineet & Alsulami, Hamed & Saeed, Tareq, 2020. "Dynamic stability analysis of stochastic fractional-order memristor fuzzy BAM neural networks with delay and leakage terms," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    2. Yao, Xueqi & Zhong, Shouming & Hu, Taotao & Cheng, Hong & Zhang, Dian, 2019. "Uniformly stable and attractive of fractional-order memristor-based neural networks with multiple delays," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 392-403.
    3. 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.
    4. 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).

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