Advanced Search
MyIDEAS: Login to save this article or follow this journal

Evolution Methods of Formation of Neuronet Models of Complex Economic Systems


Author Info

  • Khemelyov Oleksandr H.

    (Donbas State Technical University)

Registered author(s):


    The article analyses principles of formation of neuronet models of complex economic systems. It justifies prospectiveness of use of artificial intellect methods when modelling complex economic systems. It shows a possibility of use of evolution methods when forming neuronet models of complex economic systems for ensuring invariance of their generalising properties. It offers an algorithm with a genome from operons of fixed length. It considers all operons from the point of view of functional positions. It notes a specific feature of the algorithm, which allows excluding anthropogenic factors when selecting the neuronet models architecture. It proves adequacy of the formed neuronet models of complex economic systems.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL:
    Download Restriction: no

    Bibliographic Info

    Article provided by RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE), Kharkiv National University of Economics in its journal Business Inform.

    Volume (Year): (2014)
    Issue (Month): 1 ()
    Pages: 69_73

    as in new window
    Handle: RePEc:idp:bizinf:y:2014:i:1:p:69_73

    Contact details of provider:
    Web page:

    Related research

    Keywords: economic system; business process; neuronet models; evolution methods;


    No references listed on IDEAS
    You can help add them by filling out this form.



    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.


    Access and download statistics


    When requesting a correction, please mention this item's handle: RePEc:idp:bizinf:y:2014:i:1:p:69_73. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alexey Rystenko).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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