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

  • Yevgeniy BodyanskiyEmail author
  • Olena Vynokurova
  • Iryna Pliss
  • Dmytro Peleshko
  • Yuriy Rashkevych
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 470)

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.

Keywords

Computational intelligence Soft computing Wavelet neuro-fuzzy networks Hybrid intelligent systems 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yevgeniy Bodyanskiy
    • 1
    Email author
  • Olena Vynokurova
    • 1
  • Iryna Pliss
    • 1
  • Dmytro Peleshko
    • 2
  • Yuriy Rashkevych
    • 2
  1. 1.Kharkiv National University of Radio ElectronicsKharkivUkraine
  2. 2.Lviv Politechnik National UniversityLvivUkraine

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