IDEAS home Printed from https://ideas.repec.org/a/fzy/fuzeco/vviiy2002i2p81-92.html
   My bibliography  Save this article

Effectiveness Of System Identification For Complex Systems By The Fuzzy Adaptive Gmdh

Author

Listed:
  • Tomonori Nishikawa

    (Ryutsu-Keizai University)

  • Shizue Shimizu

    (Kyoei University)

  • Masafumi Imai

    (Toyohasi Sozo Senior College)

Abstract

This paper discusses and verifies effectiveness about the Fuzzy Adaptive GMDH applied the concept of the fuzzy set to the identification process of the adaptive GMDH (Group Method of Data Handling) as the modeling a complex system. The adaptive GMDH [1] is enhancing ability of basic GMDH [2] and is a system identification method by which "Ridge bias parameter ?” is introduced to avoid the multicollinearity to the GMDH, and its characteristics is improved [3,4,5]. Furthermore, when we apply this Fuzzy Adaptive GMDH to the time series data, the forecast error of the time series data has been absor-bed to the changing control limit of between upper and lower limits automatically. The adaptive GMDH developed as a modeling technique of a complex sys-tem, whose interrelations is nonlinear and more indefinite system to with respect to the factors, which fulfills its function for complex system iden-tification. In addition, it can be said that making the technique the Fuzzy Adap-tive GMDH will effectively function to the identification of a complex system.

Suggested Citation

  • Tomonori Nishikawa & Shizue Shimizu & Masafumi Imai, 2002. "Effectiveness Of System Identification For Complex Systems By The Fuzzy Adaptive Gmdh," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 81-92, November.
    • Handle: RePEc:fzy:fuzeco:v:vii:y:2002:i:2:p:81-92
    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 search for a similarly titled item that would be available.

    More about this item

    Keywords

    fuzzy; fuzzy adaptive GMDH; singular value decomposition; LP; control limits; ridge bias parameter;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:fzy:fuzeco:v:vii:y:2002:i:2:p:81-92. 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: Aurelio Fernandez (email available below). General contact details of provider: https://edirc.repec.org/data/sigefea.html .

    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.