• DocumentCode
    2221766
  • Title

    Evolved neural systems with unit breeding. Principles of development

  • Author

    Voronenko, Dmitriy I.

  • Author_Institution
    Kharkov, Ukraine
  • Volume
    1
  • fYear
    1998
  • fDate
    4-8 May 1998
  • Firstpage
    674
  • Abstract
    An approach to evolved neural systems is presented which, although preliminary in nature appears to be able to give a number of advantages over other approaches. The main feature of the approach is evolution under influence of environment. Additionally the more exact simulation of biological evolution is provided. Other features are the continual learning during functioning through self-refinement and self-adjustment and the rules for unsupervised termination of a system existence in the case of its incompetence. The proposition of classification is given for evolved neural systems developed with the presented approach. Two classes of evolved neural system are defined. The first class is the original method of use of genetic algorithm principles to evolution of neural system. The second one is the simulation of evolution of the natural nervous systems
  • Keywords
    evolution (biological); genetic algorithms; neural nets; neurophysiology; self-adjusting systems; biological evolution; evolved neural systems; natural nervous systems; self-adjustment; self-refinement; unit breeding; Artificial neural networks; Biological information theory; Biological system modeling; Biology computing; Computer vision; Evolution (biology); Genetic algorithms; Nervous system; Neural networks; Pattern recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-4859-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1998.682361
  • Filename
    682361