• DocumentCode
    2625643
  • Title

    Generalization of CNN with hysteresis nonlinearity

  • Author

    Slavova, Angela

  • Author_Institution
    Dept. of Math., Univ. of Min. & Geol., Sofia, Bulgaria
  • fYear
    1998
  • fDate
    14-17 Apr 1998
  • Firstpage
    56
  • Lastpage
    61
  • Abstract
    We introduce a general class of neural networks. This new model covers some of the known neural network architectures, including cellular neural networks and Hopfield networks. Hysteresis feedback networks are introduced and compared to the general Hopfield networks in order to prove the existence of hysteresis phenomena in the network
  • Keywords
    cellular neural nets; differential equations; feedback; generalisation (artificial intelligence); hysteresis; Hopfield networks; cellular neural networks; differential equation; feedback; generalization; hysteresis; nonlinearity; Cellular neural networks; Differential equations; Geology; Hopfield neural networks; Hysteresis; Mathematics; Neural networks; Neurofeedback; Nonlinear circuits; Nonlinear equations;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Neural Networks and Their Applications Proceedings, 1998 Fifth IEEE International Workshop on
  • Conference_Location
    London
  • Print_ISBN
    0-7803-4867-2
  • Type

    conf

  • DOI
    10.1109/CNNA.1998.685330
  • Filename
    685330
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2625643