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Information Retrieval and Data Forecasting via Probabilistic Nodes Combination

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Intelligent Data Engineering and Automated Learning – IDEAL 2015 (IDEAL 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9375))

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Abstract

Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D data interpolation and extrapolation. Nodes are treated as characteristic points of information retrieval and data forecasting. PNC modeling via nodes combination and parameter γ as probability distribution function enables 2D point extrapolation and interpolation. Two-dimensional information is modeled via nodes combination and some functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function. Extrapolated values are used as the support in data forecasting.

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Author information

Authors and Affiliations

  1. Department of Electronics and Computer Science, Technical University of Koszalin, Koszalin, Poland

    Dariusz Jacek Jakóbczak

Authors
  1. Dariusz Jacek Jakóbczak
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Corresponding author

Correspondence to Dariusz Jacek Jakóbczak .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Konrad Jackowski

  2. Department of Systems, Wroclaw University of Technology, Wroclaw, Poland

    Robert Burduk

  3. Wroclaw Univ of Tech, Wroclaw, Poland

    Krzysztof Walkowiak

  4. Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland

    Michal Wozniak

  5. School of Electrical & Electronic E, University of Manchester, Manchester, United Kingdom

    Hujun Yin

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© 2015 Springer International Publishing Switzerland

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Jakóbczak, D.J. (2015). Information Retrieval and Data Forecasting via Probabilistic Nodes Combination. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-24834-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24833-2

  • Online ISBN: 978-3-319-24834-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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