Hybrid Multidimensional Wavelet-Neuro-System and its Learning Using Cross Entropy Cost Function in Pattern Recognition

Abstract

In this paper, the hybrid multidimensional wavelet-neuro-system for pattern recognition tasks is proposed. Also learning algorithm for all its parameters (synaptic weights, the centers, and widths of wavelet activation functions) based on cross entropy cost function was proposed. The proposed system is characterized by high learning speed and high approximation properties in comparison with wellknown approaches. The efficiency of the proposed approach has been justified based on different benchmarks and real data sets.