Whole learning algorithm for feedforward neural network by Moore-Penrose Generalized Inverse

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Other Title
  • ムーア・ペンローズ一般逆行列を用いたニューラルネットワークの一括学習アルゴリズム

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Description

A new learning algorithm named whole learning algorithm is proposed for the feedforward neural network. Strictly speaking, the learning of the feedforward neural network is a kind of multi-objective optimization problem to minimize the errors of outputs for all the learning data sets with respect to the amount of weight modification. All the learning data sets are simultaneously taken into account to constitute the governing equation of the weight modification, which is formulated as linear simultaneous equations with rectangular matrix of coefficients in the proposed algorithm. The solution of the equation is determined by means of the Moore-Penrose generalized inverse to deal with the rectangular matrix. The efficiency of the proposed algorithm is demonstrated through the problem to learn the nolinear behavior described by the Ramberg-Osgood model. The applicability of the proposed algorithm is investigated in problem to learn the earthquake response of RC members.

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Details 詳細情報について

  • CRID
    1390288469025522560
  • NII Article ID
    130008056187
  • DOI
    10.11421/jsces.1999.19990025
  • ISSN
    13478826
    13449443
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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