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Incremental Stability of Neural Networks with Switched Parameters and Time Delays via Contraction Theory of Multiple Norms

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Neural Information Processing (ICONIP 2018)

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

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Abstract

In this paper, we propose a new approach to investigate the incrementally exponentially asymptotically stability and contraction property of the switched recurrently connected neural networks with time-varying delays. This method of contraction theory extends the current result from ordinary differential systems to delayed differential systems. Thus, we derive sufficient conditions of incremental stability of a class of neural networks with time-varying parameters and time-delays and its asymptotical periodicity as a consequence when the time-variation is periodic. Numerical examples are presented to illustrate the power of theoretical results.

This work is jointly supported by the National Natural Sciences Foundation of China under Grant No. 61673119.

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Authors and Affiliations

  1. School of Mathematical Sciences, Fudan University, Shanghai, 200433, China

    Hao Qiang & Wenlian Lu

Authors
  1. Hao Qiang
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  2. Wenlian Lu
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Corresponding author

Correspondence to Hao Qiang .

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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Qiang, H., Lu, W. (2018). Incremental Stability of Neural Networks with Switched Parameters and Time Delays via Contraction Theory of Multiple Norms. 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_33

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  • DOI: https://doi.org/10.1007/978-3-030-04179-3_33

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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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