• DocumentCode
    1817841
  • Title

    Piecewise linear networks (PLN) for function approximation

  • Author

    Eppler, Wolfgang ; Beck, Hans N.

  • Author_Institution
    Forschungszentrum Karlsruhe, Germany
  • Volume
    1
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    388
  • Abstract
    A piecewise linear network is a general neural network with three layers. It was designed for fast function approximation with a good generalization capability even in the case of very few data points. An intuitive understanding of the network processing is possible and the complexity of the network varies with the complexity of the function being approximated. This means that strong nonlinear functions are modelled by networks with more complex structure than the linear ones. The training of the network is constructive. The user provides only one parameter for the algorithm: the abort condition, when the training of the network should stop
  • Keywords
    computational complexity; feedforward neural nets; function approximation; generalisation (artificial intelligence); learning (artificial intelligence); computational complexity; function approximation; generalization; learning; multilayer neural network; nonlinear functions; piecewise linear network; Control system synthesis; Control systems; Function approximation; Neural networks; Neurons; Nonlinear equations; Piecewise linear approximation; Piecewise linear techniques; System identification; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.831525
  • Filename
    831525