An Estimation Framework in a Forward-propagation Learning Rule

Yoshihiro Ohama; Naohiro Fukumura; Yoji Uno

doi:10.11499/sicep.2004.0.80.1

SICE Annual Conference 2004

DOI https://doi.org/10.11499/sicep.2004.0.80.1

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

An Estimation Framework in a Forward-propagation Learning Rule

*Yoshihiro Ohama, Naohiro Fukumura, Yoji Uno

著者情報

キーワード: inverse model, neural network, learning algorithm

会議録・要旨集フリー

p. 80

詳細

抄録

A forward-propagation learning rule has been proposed to acquire neural inverse models. This rule can solve a credit assignment problem based on Newton-like method. In the current work, we discuss how to estimate the parameters of a multi-layered neural network based on the credit assignment. The suitability of the proposed estimation framework is confirmed by computer simulation.

責任著者(Corresponding author)

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