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Passivity Analysis for Neuro Identifier with Different Time-Scales

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Intelligent Computing (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4113))

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Abstract

Many physical systems contains fast and slow phenomenons. In this paper we propose a dynamic neural networks with different time-scales to model the nonlinear system. Passivity-based approach is used to derive stability conditions for neural identifer. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. Numerical examples are also given to demonstrate the effectiveness of the theoretical results.

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Author information

Authors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN, 2508, México D.F., 07360, México

    Alejandro Cruz Sandoval & Wen Yu

  2. Sección de Computación, Departamento de Ingeniería Eléctrica, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, México D.F., 07360, México

    Xiaoou Li

Authors
  1. Alejandro Cruz Sandoval
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  2. Wen Yu
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  3. Xiaoou Li
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China

    De-Shuang Huang

  2. Carnegie Mellon University,  

    Kang Li

  3. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Stranmillis Road, BT9 5AH, Belfast, UK

    George William Irwin

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© 2006 Springer-Verlag Berlin Heidelberg

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Sandoval, A.C., Yu, W., Li, X. (2006). Passivity Analysis for Neuro Identifier with Different Time-Scales. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_51

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  • DOI: https://doi.org/10.1007/11816157_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37271-4

  • Online ISBN: 978-3-540-37273-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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