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

Publication Type Publications in RTU scientific journal
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Hybrid Generalised Additive Type-2 Fuzzy-Wavelet-Neural Network in Dynamic Data Mining
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Yevgeniy Bodyanskiy
Olena Vynokurova
Iryna Pliss
Yuliia Tatarinova
Keywords Computational intelligence, evolutionary computations, fuzzy neural networks, hybrid intelligent systems
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.
Reference 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.
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