• DocumentCode
    3263347
  • Title

    A new orthogonal neural network

  • Author

    Tseng, Ching-Shiow ; Yang, Shiow-Shung

  • Author_Institution
    Dept. of Mech. Eng., Nat. Central Univ., Chung-Li, Taiwan
  • Volume
    1
  • fYear
    1995
  • fDate
    Nov/Dec 1995
  • Firstpage
    296
  • Abstract
    This paper presents a new neural network based on orthogonal functions. This single-layer neural network may avert the problems of traditional feedforward neural networks such as the determination of the numbers of layers and processing elements, and the initial values of weights. The processing elements of the neural network are composed of the expansion terms of Legendre polynomials. The required number of processing elements is determined according to the desired output accuracy. Because the weights are unique, the training of the weights will converge rapidly. Two experiments are given to demonstrate the performance of the proposed neural network. The results show that the neural network has excellent performance in convergence time and in finding a near-global solution
  • Keywords
    Legendre polynomials; function approximation; neural nets; polynomials; Legendre polynomials; convergence time; expansion terms; near-global solution; orthogonal neural network; single-layer neural network; Approximation error; Backpropagation algorithms; Convergence; Equations; Feedforward neural networks; Mechanical engineering; Multi-layer neural network; Neural networks; Polynomials; Radial basis function networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1995. Proceedings., IEEE International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-2768-3
  • Type

    conf

  • DOI
    10.1109/ICNN.1995.488112
  • Filename
    488112