Training Quasi-ARX Neural Network Model by Homotopy Approach

Jinglu HU; Xibin LU; Kotaro HIRASAWA

doi:10.11499/sicep.2004.0_1_4

SICE Annual Conference 2004

DOI https://doi.org/10.11499/sicep.2004.0_1_4

会議情報

主催: The Society of Instrument and Control Engineers

共催: The IEEE Industrial Electronics Society, The IEEE Robotics and Automation Society, The IEEE Control Systems Society, The IEEE Systems, Man and Cybernetics Society, The Instrumentation, Systems, and Automation Society

Training Quasi-ARX Neural Network Model by Homotopy Approach

*Jinglu HU, Xibin LU, Kotaro HIRASAWA

著者情報

キーワード: Neural Network, ARX Model, Homotopy Continuation Method

会議録・要旨集フリー

p. 1

詳細

抄録

This paper applies a homotopy continuation method to improving the BP training of quasi-ARX neural network model. The idea is to start the BP training with the criterion function for linear ARX model, which is gradually deformed into the actual one for quasi-ARX neural network model. The effort is made to build the deformation into a usual BP recursive procedure.

責任著者(Corresponding author)

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