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
Original language | English |
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Publication status | Published - 2004 |
Event | 66th EAGE Conference & Exhibition 2004 - Paris, France Duration: 7 Jun 2004 → 10 Jun 2004 |
Conference
Conference | 66th EAGE Conference & Exhibition 2004 |
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Country/Territory | France |
City | Paris |
Period | 7/06/04 → 10/06/04 |