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Publikācija: Least Squares Support Vector Machine Based on Wavelet-Neuron

Publication Type Publications in RTU scientific journal
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Least Squares Support Vector Machine Based on Wavelet-Neuron
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
Oleksandra Kharchenko
Keywords Adaptive wavelet function, forecasting, least squares support vector machine, non-linear non-stationary time series, wavelet-neuron.
Abstract In this paper, a simple wavelet-neuro-system that implements learning ideas based on minimization of empirical risk and oriented on information processing in on-line mode is developed. As an elementary block of such systems, we propose using wavelet-neuron that has improved approximation properties, computational simplicity, high learning rate and capability of local feature identification in data processing. The architecture and learning algorithm for least squares wavelet support machines that are characterized by high speed of operation and possibility of learning under conditions of short training set are proposed.
DOI: 10.1515/itms-2014-0002
Reference Bodyanskiy, Y., Vynokurova, O., Kharchenko, O. Least Squares Support Vector Machine Based on Wavelet-Neuron. Information Technology and Management Science. Vol.17, 2014, pp.19-24. ISSN 2255-9086. e-ISSN 2255-9094. Available from: doi:10.1515/itms-2014-0002
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