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Generalized Net Description of Essential Attributes Generator in SEAA Method

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Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 322))

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Abstract

The paper considers the generalized net description of essential attributes generator which is one of the main part of SEAA method developed for dimensionality reduction of time series. SEAA method (Symbolic Essential Attributes Approximation) (Krawczak and Szkatuła, 2014) was developed to reduce the dimensionality of multidimensional time series by generating a new nominal representation of the original data series. The approach is based on the concept of data series envelopes and essential attributes obtained by a multilayer neural network. The considered neural network architecture is based on Cybenko’s theorem and consists of two three-layer neural networks. In this paper the generalized net description of this part of SEAA method is developed in order to show the beauty of generalized nets.

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Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Maciej Krawczak & Grażyna Szkatuła

  2. Warsaw School of Information Technology, Newelska 6, Warsaw, Poland

    Maciej Krawczak

Authors
  1. Maciej Krawczak
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  2. Grażyna Szkatuła
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Corresponding author

Correspondence to Maciej Krawczak .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    P. Angelov

  2. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    K.T. Atanassov

  3. Intelligent Systems Department, Bulgarian Academy of Sciences Inst. of Infor. & Communication Techn., Sofia, Bulgaria

    L. Doukovska

  4. Metalurgy, University of Chemical Technology and, Sofia, Bulgaria

    M. Hadjiski

  5. University of Library Studies and IT (ULSIT), Sofia, Bulgaria

    V. Jotsov

  6. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    J. Kacprzyk

  7. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

    N. Kasabov

  8. Intelligent Systems Laboratory, Prof. Assen Zlatarov University Faculty of Technical Sciences, Bourgas, Bulgaria

    S. Sotirov

  9. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    E. Szmidt

  10. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    S. Zadrożny

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Krawczak, M., Szkatuła, G. (2015). Generalized Net Description of Essential Attributes Generator in SEAA Method. In: Angelov, P., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-11313-5_57

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  • DOI: https://doi.org/10.1007/978-3-319-11313-5_57

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11312-8

  • Online ISBN: 978-3-319-11313-5

  • eBook Packages: EngineeringEngineering (R0)

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