• DocumentCode
    356068
  • Title

    Maximum/minimum signal selector for competitive learning neural network

  • Author

    Abdel-Aty-Zohdy, Hoda S. ; El-Licy, Fatma A.

  • Author_Institution
    Dept. of Electr. & Syst. Eng., Oakland Univ., Rochester, MI, USA
  • Volume
    1
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    392
  • Abstract
    A sixteen-input maximum/minimum selector circuit is designed, based on multi-input current comparators, implemented in MOSIS 2 um CMOS, n-well technology. It operates with input-signal range 1.6-to-3.6 V, with a 5 V supply. The circuit is developed for possible applications in competitive learning neural networks (NNs) for sensors output interface: (1) as a preprocessor; for crude classification of sensor outputs-the circuit operates as a maximum value selector to compute the infinity norm of the distance between the input features and corresponding neural synaptic strength; (2) as a post processor, minimum distance value selector to determine the competitive winning neuron. Experimental measurements have been favorable
  • Keywords
    CMOS analogue integrated circuits; analogue processing circuits; current comparators; neural chips; unsupervised learning; 1.6 to 3.6 V; 2 micron; CMOS; competitive learning neural network; competitive winning neuron; infinity norm; input features; maximum/minimum signal selector; multi-input current comparators; n-well technology; neural synaptic strength; sensor outputs; Analog circuits; Artificial intelligence; CMOS technology; Design engineering; H infinity control; Laboratories; Microelectronics; Neural networks; Neurons; Systems engineering and theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1999. 42nd Midwest Symposium on
  • Conference_Location
    Las Cruces, NM
  • Print_ISBN
    0-7803-5491-5
  • Type

    conf

  • DOI
    10.1109/MWSCAS.1999.867288
  • Filename
    867288