A study of neural network architecture for weak non-linear modeling

Yoshiki Mizukami; Yuji Wakasa; Kanya Tanaka

doi:10.11499/sicep.2004.0_31_2

SICE Annual Conference 2004

DOI https://doi.org/10.11499/sicep.2004.0_31_2

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

A study of neural network architecture for weak non-linear modeling

*Yoshiki Mizukami, Yuji Wakasa, Kanya Tanaka

著者情報

会議録・要旨集フリー

p. 31

詳細

抄録

This paper studies a property of neural network architecture for non-linear modeling. This method was proposed in our previous work and has three improvements; 1) the design of a sigmoidal function with localized derivative, 2) a deterministic scheme for weight initialization, and 3) an updating rule for weight parameters. We discuss its robustness against noise based on simulation results.

責任著者(Corresponding author)

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