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Hybrid Generalised Additive Type-2 Fuzzy-Wavelet-Neural Network in Dynamic Data Mining

Author

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  • Bodyanskiy Yevgeniy
  • Vynokurova Olena
  • Pliss Iryna
  • Tatarinova Yuliia

    (Kharkiv National University of Radio Electronics)

Abstract

In the paper, a new hybrid system of computational intelligence is proposed. This system combines the advantages of neuro-fuzzy system of Takagi-Sugeno-Kang, type-2 fuzzy logic, wavelet neural networks and generalised additive models of Hastie-Tibshirani. The proposed system has universal approximation properties and learning capability based on the experimental data sets which pertain to the neural networks and neuro-fuzzy systems; interpretability and transparency of the obtained results due to the soft computing systems and, first of all, due to type-2 fuzzy 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 generalised 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 characterised by a high speed of learning and information processing.

Suggested Citation

  • Bodyanskiy Yevgeniy & Vynokurova Olena & Pliss Iryna & Tatarinova Yuliia, 2015. "Hybrid Generalised Additive Type-2 Fuzzy-Wavelet-Neural Network in Dynamic Data Mining," Information Technology and Management Science, Sciendo, vol. 18(1), pages 70-77, December.
    • Handle: RePEc:vrs:itmasc:v:18:y:2015:i:1:p:70-77:n:11
    DOI: 10.1515/itms-2015-0011
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