A hierarchical method for training embedded sigmoidal neural networks

Jinglu Hu, Kotaro Hirasawa

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

This paper discusses the problem of applying sigmoidal neural networks to identification of nonlinear dynamical systems. When using sigmoidal neural networks directly as nonlinear models, one often meets problems such as model parameters lack of physical meaning, sensitivity to noise in model training. In this paper, we introduce an embedded sigmoidal neural network model, in which the neural network is not used directly as a model, but is embedded in a shield such that part of the model parameters become meaningful. Corresponding to the meaningful part and the meaningless part of model parameters, a hierarchical training algorithm consisting of two learning loops is then introduced to train the model. Simulation results show that such a dual loop learning algorithm can solve the noise sensitivity and local minimum problems to some extent.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks - ICANN 2001 - International Conference, Proceedings
編集者Kurt Hornik, Georg Dorffner, Horst Bischof
出版社Springer Verlag
ページ937-942
ページ数6
ISBN(印刷版)3540424865, 9783540446682
DOI
出版ステータスPublished - 2001
外部発表はい
イベントInternational Conference on Artificial Neural Networks, ICANN 2001 - Vienna, Austria
継続期間: 2001 8 212001 8 25

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2130
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

OtherInternational Conference on Artificial Neural Networks, ICANN 2001
国/地域Austria
CityVienna
Period01/8/2101/8/25

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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