Computational Neural Networks for Geophysical Data Processing

Released on by M.M. Poulton
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  • Computational Neural Networks for Geophysical Data Processing Book Detail

  • Author : M.M. Poulton
  • Release Date : 2001-06-13
  • Publisher : Elsevier
  • Genre : Science
  • Pages : 351
  • ISBN 13 : 0080529658
  • File Size : 65,65 MB
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Computational Neural Networks for Geophysical Data Processing by M.M. Poulton PDF Summary

Book Description: This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, this work can provide a wide range of examples of nuances in network design, data set design, testing strategy, and error analysis. Computational, rather than artificial, modifiers are used for neural networks in this book to make a distinction between networks that are implemented in hardware and those that are implemented in software. The term artificial neural network covers any implementation that is inorganic and is the most general term. Computational neural networks are only implemented in software but represent the vast majority of applications. While this book cannot provide a blue print for every conceivable geophysics application, it does outline a basic approach that has been used successfully.

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Computational Geo-Electromagnetics

Computational Geo-Electromagnetics

File Size : 58,58 MB
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Computational Geo-Electromagnetics: Methods, Models, and Forecasts, Volume Five in the Computational Geophysics series, is devoted to techniques for building of