• DocumentCode
    1646765
  • Title

    The research on learning algorithm of binary neural networks

  • Author

    Hua, Qiang ; Zheng, Qi-Lun

  • Author_Institution
    Huizhou Univ., Guangdong, China
  • Volume
    1
  • fYear
    2002
  • fDate
    6/24/1905 12:00:00 AM
  • Firstpage
    541
  • Lastpage
    546
  • Abstract
    HGLA (Hamming-graph learning algorithm) is an unexhausted learning algorithm of binary neural networks. It affords the methods of using hidden neurons to express the most number of samples; the formula to calculate the weights and threshold; and the output neuron needs no training, so it is optimized
  • Keywords
    graph theory; learning (artificial intelligence); neural nets; HGLA; Hamming-graph learning algorithm; binary neural networks; hidden neuron; Algorithm design and analysis; Binary codes; Hamming distance; Input variables; Mercury (metals); Neural networks; Neurons; Optimization methods; Power line communications;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
  • Conference_Location
    Honolulu, HI
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7278-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.2002.1005530
  • Filename
    1005530
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1646765