• DocumentCode
    1907400
  • Title

    A theory on a neural net with nonmonotone neurons

  • Author

    Yanai, Hiro-F ; Amari, Shun-Ichi

  • Author_Institution
    Dept. of Inf. & Commun. Eng., Tamagawa Univ., Tokyo, Japan
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    1385
  • Abstract
    A theoretical equation on dynamical processes on a neural net consisting of neurons with two-stage nonlinear dynamics is shown. The neurons have nonmonotone response characteristics when parameters are chosen as such. By the exact solution, the high performance of the auto-associative memory net consisting of nonmonotone neurons within a general framework is proved. A correspondence of the dynamical recalling processes of nonmonotone neurons with the learning rule of orthogonal-projection type is shown. Based on the correspondence, it is possible to understand intuitively why nonmonotone neurons are effective
  • Keywords
    content-addressable storage; learning (artificial intelligence); neural nets; auto-associative memory net; dynamical processes; learning rule; neural net; nonmonotone neurons; nonmonotone response characteristics; orthogonal-projection type; recalling processes; two-stage nonlinear dynamics; Associative memory; Computer simulation; Equations; History; Hysteresis; Mathematical model; Neural networks; Neurons; Pattern analysis; Time factors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993., IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0999-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1993.298759
  • Filename
    298759