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Parameter Estimation Using a SCE Strategy

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Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5864))

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Abstract

As a novel evolutionary computation technique, shuffled complex evolution (SCE) has attracted much attention and wide applications for solving complex optimization problems in different fields. This paper utilizes SCE strategy to estimate parameters of structural systems, which could be formulated as a multi-modal optimization problem with high dimension. Simulation results for identifying the parameters of a structural system under four conditions including limited output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. Research Institute of Structural Engineering and Disaster Reduction, Tongji University, Shanghai, 200092, PRC

    Pengfei Li, Hesheng Tang & Zhaoliang Wang

Authors
  1. Pengfei Li
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  2. Hesheng Tang
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  3. Zhaoliang Wang
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

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© 2009 Springer-Verlag Berlin Heidelberg

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Li, P., Tang, H., Wang, Z. (2009). Parameter Estimation Using a SCE Strategy. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_12

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  • DOI: https://doi.org/10.1007/978-3-642-10684-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10682-8

  • Online ISBN: 978-3-642-10684-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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