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Comparison of Adaptive Algorithms for Significant Feature Selection in Neural Network Based Solution of the Inverse Problem of Electrical Prospecting

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Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5769))

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Abstract

One of the important directions of research in geophysical electrical prospecting is solution of inverse problems (IP), in particular, the IP of magnetotellurics – the problem of determining the distribution of electrical conductivity in the thickness of earth by the values of electromagnetic field induced by ionosphere sources, observed on earth surface. Solution of this IP is hampered by very high dimensionality of the input data (~103–104). Selection of the most significant features for each determined parameter makes it possible to simplify the IP and to increase the precision of its solution. This paper presents a comparison of two modifications of the developed algorithm for multi-step selection of significant features and the results of their application.

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References

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Author information

Authors and Affiliations

  1. D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Leninskie Gory, Moscow, 119991, Russia

    Sergey Dolenko, Alexander Guzhva & Igor Persiantsev

  2. S.Ordjonikidze Russian State Geological Prospecting University, RSGPU, 23 Miklukho-Maklai st., Moscow, 117997, Russia

    Eugeny Obornev & Mikhail Shimelevich

Authors
  1. Sergey Dolenko
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  2. Alexander Guzhva
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  3. Eugeny Obornev
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  4. Igor Persiantsev
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  5. Mikhail Shimelevich
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Dolenko, S., Guzhva, A., Obornev, E., Persiantsev, I., Shimelevich, M. (2009). Comparison of Adaptive Algorithms for Significant Feature Selection in Neural Network Based Solution of the Inverse Problem of Electrical Prospecting. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_40

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  • DOI: https://doi.org/10.1007/978-3-642-04277-5_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04276-8

  • Online ISBN: 978-3-642-04277-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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