Identification of Nonlinear Dynamic Models with Partially Connected Neural Networks Trained Using Orthogonal Least Square Estimation

• Rajapakse Athula

  University of Tokyo

• Furuta Kazuo

  University of Tokyo

• Kondo Shunsuke

  University of Tokyo

抄録

A methodology for identifying nonlinear dynamic systems using NARX model structure and three layered feed forward neural networks is presented. The neural network, which maps between regressor vector of the NARX model and the output, is viewed as comprising of number of linear models, each of which is represented by a hidden neuron. An algorithm that uses orthogonal least squares based regressor selection procedure for the estimation of the structure and weights of the hidden neurons is developed for training partially connected neural networks. The use of regressor selection algorithm facilitates implicit identification of unknown dead times and dynamic orders by the neural network during the training process. The conventional error back propagation is used for online adaptation of the identified neural network model. The validity of the proposed approach is demonstrated by applying it to obtain the model of a wastewater pH neutralization process.

収録刊行物

• 電気学会論文誌Ｃ（電子・情報・システム部門誌）

  電気学会論文誌Ｃ（電子・情報・システム部門誌） 119 (3), 335-343, 1999

  一般社団法人 電気学会

キーワード

• Nonlinear model identification
• Neural networks
• Orthogonal least squares

詳細情報

• CRID: 1390001204611441920
• NII論文ID: 130006845945
• NII論文ID: 10002816417
• NII書誌ID: AN10065950
• DOI: 10.1541/ieejeiss1987.119.3_335
• ISSN: 13488155
• ISSN: 03854221
• NDL書誌ID: 4663631
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I4663631
• Web Site: https://www.jstage.jst.go.jp/article/ieejeiss1987/119/3/119_3_335/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可