IDEAS home Printed from https://ideas.repec.org/a/caa/jnlage/v52y2006i10id5051-agricecon.html
   My bibliography  Save this article

Toward efficient modeling of fuzzy expert systems: a survey

Author

Listed:
  • S. Aly

    (Czech University of Agriculture, Prague, Czech Republic)

  • I. Vrana

    (Czech University of Agriculture, Prague, Czech Republic)

Abstract

Efficient modeling of the artificial intelligence tools has become a necessity in order to cut down the development and maintenance cost associated with building application systems in the business, industrial and agriculture sectors that are frequently amendable to sudden unexpected environmental and economic conditions changes. This can be accomplished through developing an efficient modeling language which exploits the beneficial features of the emerging object-oriented technology. This research is aimed at reviewing the recent scientific aspects of the research concerning conceptual modeling of fuzzy knowledge-based system, which exhibits a large extent of applicability in last few decades due to its capability to deal with vagueness, uncertainty and subjectivity, those are inherent in real world problems. The most recent researches and applications of fuzzy expert system are surveyed. The existing knowledge modeling techniques are reviewed and the prominent ones are pinpointed. This paper is intended to identify the main and common bottlenecks of the existing knowledge modeling tools to overcome it in developing a reliable conceptual model of fuzzy expert system.

Suggested Citation

  • S. Aly & I. Vrana, 2006. "Toward efficient modeling of fuzzy expert systems: a survey," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 52(10), pages 456-460.
  • Handle: RePEc:caa:jnlage:v:52:y:2006:i:10:id:5051-agricecon
    DOI: 10.17221/5051-AGRICECON
    as

    Download full text from publisher

    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/5051-AGRICECON.html
    Download Restriction: free of charge

    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/5051-AGRICECON.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.17221/5051-AGRICECON?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kun Chang Lee & Jae Ho Han & Yong Uk Song & Won Jun Lee, 1998. "A fuzzy logic‐driven multiple knowledge integration framework for improving the performance of expert systems," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(4), pages 213-222, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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:caa:jnlage:v:52:y:2006:i:10:id:5051-agricecon. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

      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.