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A New Lyapunov Analysis of Robust Stability of Neural Networks with Discrete Time Delays

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

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Abstract

This paper studies the global asymptotic robust stability of dynamical neural networks with discrete time delays under parameter uncertainties. By utilising the Lyapunov stability and Homeomorphic mapping theorems, a new sufficient condition is presented for the existence, uniqueness and global robust asymptotic stability of this class of neural systems with respect to the Lipschitz continuous activation functions. The proposed stability criterion is derived by employing a new type of Lyapunov functional and it unifies some of the key robust stability results obtained in the past literature.

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Acknowledgements

This study was funded by Scientific Research Projects Coordination Unit of Istanbul University-Cerrahpasa. Project numbers: BYP-2020-34652

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey

    Sabri Arik

Authors
  1. Sabri Arik
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Correspondence to Sabri Arik .

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Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Arik, S. (2020). A New Lyapunov Analysis of Robust Stability of Neural Networks with Discrete Time Delays. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_41

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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