Least Squares Support Vector Machine Based on Wavelet-Neuron

Yevgeniy Bodyanskiy, Olena Vynokurova, Oleksandra Kharchenko


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.


Adaptive wavelet function; forecasting; least squares support vector machine; non-linear non-stationary time series; wavelet-neuron

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