A Systematic Approach to the Optimal Design of Feed Forward Neural Networks Applied to Log-Synthesis

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A Systematic Approach to the Optimal Design of Feed Forward Neural Networks Applied to Log-Synthesis. / Fruhwirth, Rudolf Konrad; Steinlechner, Sepp Peter.

2004. Paper presented at 66th EAGE Conference & Exhibition 2004, Paris, France.

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Fruhwirth, RK & Steinlechner, SP 2004, 'A Systematic Approach to the Optimal Design of Feed Forward Neural Networks Applied to Log-Synthesis', Paper presented at 66th EAGE Conference & Exhibition 2004, Paris, France, 7/06/04 - 10/06/04.

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@conference{5979484768864f4b827f1fb67a9fc2f3,
title = "A Systematic Approach to the Optimal Design of Feed Forward Neural Networks Applied to Log-Synthesis",
abstract = "G001 A SYSTEMATIC APPROACH TO THE OPTIMAL DESIGN OF FEED FORWARD NEURAL NETWORKS APPLIED TO LOG-SYNTHESIS Abstract 1 Neural networks are increasingly used in geophysical applications. Optimizing neural networks is still a matter of experience and trial and error where network initialization and network size are the most challenging issues. We expanded conventional learning rules to a completely forward connected network including input neurons for automatic normalization of the data. In addition we developed a method for the network initialization based on the statistical properties of the input and output data generating an initial network state that ascertains a fast",
author = "Fruhwirth, {Rudolf Konrad} and Steinlechner, {Sepp Peter}",
year = "2004",
language = "English",
note = "null ; Conference date: 07-06-2004 Through 10-06-2004",

}

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TY - CONF

T1 - A Systematic Approach to the Optimal Design of Feed Forward Neural Networks Applied to Log-Synthesis

AU - Fruhwirth, Rudolf Konrad

AU - Steinlechner, Sepp Peter

PY - 2004

Y1 - 2004

N2 - G001 A SYSTEMATIC APPROACH TO THE OPTIMAL DESIGN OF FEED FORWARD NEURAL NETWORKS APPLIED TO LOG-SYNTHESIS Abstract 1 Neural networks are increasingly used in geophysical applications. Optimizing neural networks is still a matter of experience and trial and error where network initialization and network size are the most challenging issues. We expanded conventional learning rules to a completely forward connected network including input neurons for automatic normalization of the data. In addition we developed a method for the network initialization based on the statistical properties of the input and output data generating an initial network state that ascertains a fast

AB - G001 A SYSTEMATIC APPROACH TO THE OPTIMAL DESIGN OF FEED FORWARD NEURAL NETWORKS APPLIED TO LOG-SYNTHESIS Abstract 1 Neural networks are increasingly used in geophysical applications. Optimizing neural networks is still a matter of experience and trial and error where network initialization and network size are the most challenging issues. We expanded conventional learning rules to a completely forward connected network including input neurons for automatic normalization of the data. In addition we developed a method for the network initialization based on the statistical properties of the input and output data generating an initial network state that ascertains a fast

M3 - Paper

Y2 - 7 June 2004 through 10 June 2004

ER -