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Robust Prediction with ANNBFIS System

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Intelligent Information and Database Systems (ACIIDS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5991))

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Abstract

In this paper a learning method of Artificial Neural Network Based on Fuzzy Inference System (ANNBFIS) is presented. It is based on deterministic annealing, ε−insensitive learning by solving a system of linear inequalities, and robust fuzzy c-means clustering. To find the unknown number of fuzzy if-then rules we proposed the procedure of robust clusters merging. The performance of the learning method was demonstrated through the benchmark sunspot prediction problem.

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Author information

Authors and Affiliations

  1. Institute of Electronics, Silesian University of Technology, ul Akademicka 16, 44-101, Gliwice, Poland

    Robert Czabanski & Michal Jezewski

  2. Department of Biomedical Informatics, Institute of Medical Technology and Equipment, ul. Roosevelta 118, 41-800, Zabrze, Poland

    Krzysztof Horoba, Janusz Jezewski & Janusz Wrobel

Authors
  1. Robert Czabanski
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  2. Michal Jezewski
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  3. Krzysztof Horoba
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  4. Janusz Jezewski
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  5. Janusz Wrobel
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Editor information

Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str. Wyb. Wyspianskiego 27, 50-370, Poland

    Ngoc Thanh Nguyen

  2. Hue University, Str. Le Loi 3, Hue City, Vietnam

    Manh Thanh Le

  3. Faculty of Computer Science and Management, Wroclaw University of Technology, Str. Lukasiewicza, 50-370, Wroclaw, Poland

    Jerzy Świątek

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Czabanski, R., Jezewski, M., Horoba, K., Jezewski, J., Wrobel, J. (2010). Robust Prediction with ANNBFIS System. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds) Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science(), vol 5991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12101-2_20

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  • DOI: https://doi.org/10.1007/978-3-642-12101-2_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12100-5

  • Online ISBN: 978-3-642-12101-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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