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Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process

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Rough Sets and Intelligent Systems Paradigms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8537))

Abstract

The article presents the reduction problems of hydrographic big data for the needs of gathering sound information for Navigation Electronic Chart (ENC) production. For the article purposes, data was used from an interferometric sonar, which is a modification of a multi-beam sonar. Data reduction is a procedure meant to reduce the size of the data set, in order to make them easier and more effective for the purposes of the analysis. The authors‘ aim is to examine whether artificial neural networks can be used for clustering data in the resultant algorithm. Proposed solution based on Kohonen network is tested and described. Experimental results of investigation of optimal network configuration are presented.

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Author information

Authors and Affiliations

  1. Marine Technology Ltd., Szczecin, Poland

    Andrzej Stateczny

  2. Maritime University, Szczecin, Poland

    Marta Wlodarczyk-Sielicka

Authors
  1. Andrzej Stateczny
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  2. Marta Wlodarczyk-Sielicka
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz  & Zbigniew W. Raś  & 

  2. Department of Computer Science and Artificial Intelligence, University of Granada, Calle del Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Chris Cornelis

  3. DISCo, Università di Milano – Bicocca, Viale Sarca 336 – U14, 20126, Milano, Italy

    Davide Ciucci

  4. Dpt. de Matemáticas, University of Càdiz, Spain

    Jesús Medina-Moreno

  5. School of Computing and Information Systems, University of Tasmania, Japan

    Hiroshi Motoda

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Stateczny, A., Wlodarczyk-Sielicka, M. (2014). Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_34

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  • DOI: https://doi.org/10.1007/978-3-319-08729-0_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08728-3

  • Online ISBN: 978-3-319-08729-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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