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

Publikationen: KonferenzbeitragPaper(peer-reviewed)

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

Details

OriginalspracheEnglisch
StatusVeröffentlicht - 2004
Veranstaltung66th EAGE Conference & Exhibition 2004 - Paris, Frankreich
Dauer: 7 Jun 200410 Jun 2004

Konferenz

Konferenz66th EAGE Conference & Exhibition 2004
Land/GebietFrankreich
OrtParis
Zeitraum7/06/0410/06/04