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Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays

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Abstract

In this paper, the problem of finite-time stability for a class of fractional-order Cohen–Grossberg BAM neural networks with time delays is investigated. Using some inequality techniques, differential mean value theorem and contraction mapping principle, sufficient conditions are presented to ensure the finite-time stability of such fractional-order neural models. Finally, a numerical example and simulations are provided to demonstrate the effectiveness of the derived theoretical results.

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Acknowledgements

The support of the UAE University, through UPAR and center-based research projects, to execute this work is highly acknowledged and appreciated. The authors would like to thank the editor and reviewers for their constructive comments and suggestions which improved the manuscript.

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Authors and Affiliations

  1. Department of Mathematical Sciences, College of Science, United Arab Emirates University, 15551, Al-Ain, UAE

    C. Rajivganthi & F. A. Rihan

  2. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong Waurn Ponds Campus, Geelong, Australia

    S. Lakshmanan

  3. Department of Mathematics, Gandhigram Rural Institute-Deemed University, Gandhigram, Tamilnadu, 624 302, India

    P. Muthukumar

Authors
  1. C. Rajivganthi
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  2. F. A. Rihan
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  3. S. Lakshmanan
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  4. P. Muthukumar
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Corresponding author

Correspondence to F. A. Rihan.

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Rajivganthi, C., Rihan, F.A., Lakshmanan, S. et al. Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays. Neural Comput & Applic 29, 1309–1320 (2018). https://doi.org/10.1007/s00521-016-2641-9

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  • DOI: https://doi.org/10.1007/s00521-016-2641-9

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