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A Fast Input Selection Algorithm for Neural Modeling of Nonlinear Dynamic Systems

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 3644))

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Abstract

In neural modeling of non-linear dynamic systems, the neural inputs can include any system variable with time delays. To obtain the optimal subset of inputs regarding a performance measure is a combinational problem, and the selection process can be very time-consuming. In this paper, neural input selection is transformed into a model selection problem and a new fast input selection method is used. This method is then applied to the neural modeling of a continuous stirring tank reactor (CSTR) to confirm its effectiveness.

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Author information

Authors and Affiliations

  1. School of Electrical & Electronic Engineering, Queen’s University Belfast, Ashby Building, Stranmillis Road, Belfast, BT9 5AH, UK

    Kang Li & Jian Xun Peng

Authors
  1. Kang Li

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  2. Jian Xun Peng

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Editor information

Editors and Affiliations

  1. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences,, China

    De-Shuang Huang

  2. School of Computer & Information Technology, Beijing Jiaotong University, 100044, Beijing, P.R. China

    Xiao-Ping Zhang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, P.O. Box, Singapore

    Guang-Bin Huang

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© 2005 Springer-Verlag Berlin Heidelberg

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Li, K., Peng, J.X. (2005). A Fast Input Selection Algorithm for Neural Modeling of Nonlinear Dynamic Systems. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_108

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