A New Learning Method Using Prior Information of Neural Networks

DOI HANDLE Open Access
  • Lu Baiquan
    Venture Business Laboratory Kyushu University
  • Hirasawa Kotaro
    Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University
  • Murata Junichi
    Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University
  • Hu Jinglu
    Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University

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Abstract

In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their inter-relation of weights values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. Then we present a new learning method that trains the part of the weights and calculates the others by using these exact mathematical equations. This method often keeps a priori given mathematical structure exactly during the learning, in other words, training is done so that the network follows predetermined trajectory. Numerical computer simulation results are provided to support the present approaches.

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Details 詳細情報について

  • CRID
    1390290699820225024
  • NII Article ID
    110000579904
  • NII Book ID
    AN10569524
  • DOI
    10.15017/1498417
  • ISSN
    21880891
    13423819
  • HANDLE
    2324/1498417
  • Text Lang
    en
  • Data Source
    • JaLC
    • IRDB
    • CiNii Articles
  • Abstract License Flag
    Allowed

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