• DocumentCode
    2708179
  • Title

    Introduction to adaptive weight lattice neural networks

  • Author

    Neville, R.S. ; Luk, P.C.K.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Hertfordshire Univ., Hatfield, UK
  • Volume
    2
  • fYear
    1998
  • fDate
    4-9 May 1998
  • Firstpage
    1377
  • Abstract
    Research into RAM-based neural networks has now been in progress for approximately two decades. In this paper we introduce a novel way to visualise RAM-based neural networks. We also present an alternative way to visualise the modus operandi of these units. The main reason for this is to provide an insight into their properties and dynamical behaviour, which leads to the development of a new visualisation theory of these units as adaptive weight lattice. The investigation aims to shed light on RAM-based neural networks viewed as adaptive weight lattices nets, in order to give a qualitative insight to the international community
  • Keywords
    adaptive systems; generalisation (artificial intelligence); neural nets; pattern classification; random-access storage; RAM-based neural networks; adaptive weight lattice networks; dynamical behaviour; generalisation; pattern classification; Adaptive systems; Artificial neural networks; Hypercubes; Lattices; Neural networks; Neurons; Phase change random access memory; Resistors; Resumes; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-4859-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1998.685976
  • Filename
    685976