IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-15-5403-2_8.html

PCA and GA Based ARX Plus RBF Modeling for Nonlinear DPS

In: DNA Computing Based Genetic Algorithm

Author

Listed:
  • Jili Tao

    (NingboTech University, School of Information Science and Engineering)

  • Ridong Zhang

    (Hangzhou Dianzi University, The Belt and Road Information Research Institute)

  • Yong Zhu

    (NingboTech University, School of Information Science and Engineering)

Abstract

Distributed parameter systems (DPSs) are difficult to model due to their nonlinearity and infinite-dimension characteristics. This chapter adopts principal component analysis (PCA) to derive a hybrid modeling strategy for modeling such systems. The strategy consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear Radial Basis Function (RBF) neural network model.

Suggested Citation

  • Jili Tao & Ridong Zhang & Yong Zhu, 2020. "PCA and GA Based ARX Plus RBF Modeling for Nonlinear DPS," Springer Books, in: DNA Computing Based Genetic Algorithm, chapter 0, pages 193-220, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-5403-2_8
    DOI: 10.1007/978-981-15-5403-2_8
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-981-15-5403-2_8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.