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Hybrid Generalized Additive Wavelet-Neuro-Fuzzy-System and Its Adaptive Learning

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Dependability Engineering and Complex Systems (DepCoS-RELCOMEX 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 470))

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Abstract

In the paper, a new hybrid generalized additive wavelet-neuro-fuzzy-system of computational intelligence and its learning algorithms are proposed. This system combines the advantages of neuro-fuzzy system of Takagi-Sugeno-Kang, wavelet neural networks and generalized additive models of Hastie-Tibshirani. The proposed system has universal approximation properties and learning capabilities which pertain to the neural networks and neuro-fuzzy systems; interpretability and transparency of the obtained results due to the soft computing systems; possibility of effective description of local signal and process features due to the application of systems based on wavelet transform; simplicity and speed of learning process due to generalized additive models. The proposed system can be used for solving a wide class of dynamic data mining tasks, which are connected with non-stationary, nonlinear stochastic and chaotic signals. Such a system is sufficiently simple in numerical implementation and is characterized by a high speed of learning and information processing.

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Author information

Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, 14 Nauky av., Kharkiv, 61166, Ukraine

    Yevgeniy Bodyanskiy, Olena Vynokurova & Iryna Pliss

  2. Lviv Politechnik National University, 12 Bandera Street, Lviv, 79013, Ukraine

    Dmytro Peleshko & Yuriy Rashkevych

Authors
  1. Yevgeniy Bodyanskiy
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  2. Olena Vynokurova
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  3. Iryna Pliss
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  4. Dmytro Peleshko
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  5. Yuriy Rashkevych
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Corresponding author

Correspondence to Yevgeniy Bodyanskiy .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Wrocław University of Technology, Wrocław, Poland

    Wojciech Zamojski

  2. Department of Computer Engineering, Wrocław University of Technology, Wrocław, Poland

    Jacek Mazurkiewicz

  3. Department of Computer Engineering, Wrocław University of Technology, Wroclaw, Poland

    Jarosław Sugier

  4. Department of Computer Engineering, Wrocław University of Technology, Wrocław, Poland

    Tomasz Walkowiak

  5. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Janusz Kacprzyk

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Bodyanskiy, Y., Vynokurova, O., Pliss, I., Peleshko, D., Rashkevych, Y. (2016). Hybrid Generalized Additive Wavelet-Neuro-Fuzzy-System and Its Adaptive Learning. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Dependability Engineering and Complex Systems. DepCoS-RELCOMEX 2016. Advances in Intelligent Systems and Computing, vol 470. Springer, Cham. https://doi.org/10.1007/978-3-319-39639-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-39639-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39638-5

  • Online ISBN: 978-3-319-39639-2

  • eBook Packages: EngineeringEngineering (R0)

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