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

Rudolf Konrad Fruhwirth, Sepp Peter Steinlechner

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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
Original languageEnglish
Publication statusPublished - 2004
Event66th EAGE Conference & Exhibition 2004 - Paris, France
Duration: 7 Jun 200410 Jun 2004


Conference66th EAGE Conference & Exhibition 2004

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