• DocumentCode
    2415498
  • Title

    Orthonormal Basis Lattice Neural Networks

  • Author

    Barmpoutis, Angelos ; Ritter, Gerhard X.

  • Author_Institution
    Univ. of Florida, Gainesville
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    331
  • Lastpage
    336
  • Abstract
    Lattice based neural networks are capable of resolving some difficult non-linear problems and have been successfully employed to solve real-world problems. In this paper a novel model of a lattice neural network (LNN) is presented. This new model generalizes the standard basis lattice neural network (SB-LNN) based on dendritic computing. In particular, we show how each neural dendrite can work on a different orthonormal basis than the other dendrites. We present experimental results that demonstrate superior learning performance of the new Orthonormal Basis Lattice Neural Network (OB-LNN) over SB-LNNs.
  • Keywords
    learning (artificial intelligence); neural nets; dendritic computing; nonlinear problem; orthonormal basis lattice neural network; superior learning performance; Artificial neural networks; Biological neural networks; Biological system modeling; Biology computing; Brain modeling; Computer networks; Fuzzy logic; Lattices; Neural networks; Neurons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2006 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9488-7
  • Type

    conf

  • DOI
    10.1109/FUZZY.2006.1681733
  • Filename
    1681733