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Stability and Stabilization of Time-Delayed Fractional Order Neural Networks via Matrix Measure

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

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Abstract

The stability problem of delayed neural networks with fractional order dynamics has been studied in this paper. Several criteria for the stability of the equilibrium point are derived via matrix measure method and fractional order differential inequality. All criteria are formed as matrix measure, which can be easy to verify in practice. Based on which, feedback controllers are designed to stabilize a kind of chaotic fractional order neural network. Finally, two simulations are given to check the theoretical results and compare with some exist results.

Y. Yang—This work was jointly supported by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20161126, the Graduate Innovation Project of Jiangsu Province under Grant No. KYLX16\(_{-}\)0778.

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Author information

Authors and Affiliations

  1. School of Science, Jiangnan University, Wuxi, 214122, China

    Fei Wang & Yongqing Yang

  2. School of Mathematics, Southeast University, Nanjing, 210096, China

    Jianquan Lu & Jinde Cao

Authors
  1. Fei Wang
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  2. Yongqing Yang
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  3. Jianquan Lu
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  4. Jinde Cao
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Corresponding author

Correspondence to Yongqing Yang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Wang, F., Yang, Y., Lu, J., Cao, J. (2017). Stability and Stabilization of Time-Delayed Fractional Order Neural Networks via Matrix Measure. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_58

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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