• DocumentCode
    446548
  • Title

    Intensity-position neural network

  • Author

    Degeratu, Vasile ; Degeratu, Stefania ; Schiopu, Paul

  • Author_Institution
    Fac. of Electron. & Telecommun., "Politehnica" Univ. of Bucharest, Romania
  • Volume
    1
  • fYear
    2005
  • fDate
    3-5 Oct. 2005
  • Firstpage
    141
  • Abstract
    In this paper the authors present an intensity-position neural network. This neural network is achieved from Fabry-Perot cavities with nonlinear medium. This intensity-position neural network has many applications: it can be used into artificial systems that process external or internal stimuli by different intensities achieving decoding of these stimuli, it can be used for neural computers into process by coding of information, it can be used in the color television etc. The advantages of the presented intensity-position neural network are the following: low power of input beam for nonlinearity stimulation (tens by nano-watts), small switching time (by pico-second time), parallel addressing and diminishing of bit dimension until to limit by input beam focusing etc.
  • Keywords
    Fabry-Perot resonators; artificial intelligence; decoding; encoding; nonlinear media; optical neural nets; Fabry-Perot cavities; artificial systems; bit dimension diminishing; external stimuli processing; information coding; input beam focusing; intensity-position neural network; internal stimuli processing; low-power input beam; neural computers; nonlinear medium; nonlinearity stimulation; parallel addressing; small switching time; stimuli decoding; Artificial neural networks; Biological neural networks; Computer networks; Electronic mail; Frequency; Humans; Information analysis; Neural networks; Neurons; Organisms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Semiconductor Conference, 2005. CAS 2005 Proceedings. 2005 International
  • Print_ISBN
    0-7803-9214-0
  • Type

    conf

  • DOI
    10.1109/SMICND.2005.1558731
  • Filename
    1558731