Hybrid Generalised Additive Type-2 Fuzzy-Wavelet-Neural Network in Dynamic Data Mining
2015
Yevgeniy Bodyanskiy, Olena Vynokurova, Iryna Pliss, Yuliia Tatarinova

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.


Keywords
Computational intelligence, evolutionary computations, fuzzy neural networks, hybrid intelligent systems

Bodyanskiy, Y., Vynokurova, O., Pliss, I., Tatarinova, Y. Hybrid Generalised Additive Type-2 Fuzzy-Wavelet-Neural Network in Dynamic Data Mining. Information Technology and Management Science. Vol.18, 2015, pp.70-77. ISSN 2255-9086. e-ISSN 2255-9094.

Publication language
English (en)
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