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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 353–360Cite as

A Novel Criterion for Global Asymptotic Stability of Neutral-Type Neural Networks with Discrete Time Delays

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11302))

Abstract

The main target in this work is to propose a new condition for global asymptotic stability for neutral-type neural networks including constant delay parameters. This newly obtained criterion is derived by making the use a properly modified Lyapunov functional with the employment of Lipschitz activation functions, which establishes a new set of relationships among the constant system parameters of this class of neural networks. The obtained condition is expressed independently of delay parameters and can be easily approved by simply checking the validity of some algebraic equations.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Istanbul University, Istanbul, Turkey

    Ozlem Faydasicok

  2. Department of Computer Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey

    Sabri Arik

Authors
  1. Ozlem Faydasicok
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  2. Sabri Arik
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Corresponding author

Correspondence to Sabri Arik .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

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

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Faydasicok, O., Arik, S. (2018). A Novel Criterion for Global Asymptotic Stability of Neutral-Type Neural Networks with Discrete Time Delays. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_31

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  • DOI: https://doi.org/10.1007/978-3-030-04179-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04178-6

  • Online ISBN: 978-3-030-04179-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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