Wavelet-based signal approximation with multilevel learning algorithms using genetic neuron selection
Jing-Wein Wang, Jeng‐Shyang Pan, C.H. Chen, H.L. Fang · 2002
Neural networks based on wavelets are constructed to study the function learning problems. Two types of learning algorithms, the overall multilevel learning (OML) and the pyramidal multilevel learning (PML) with genetic neuron selection are comparatively studied for the convergence rate and accuracy using data samples of a piecewise defined signal. Moreover, the two algorithms are examined using orthogonal and non orthogonal bases. Experimental studies exhibit that the string representation of genetic algorithms (GA) is a key issue in determining the suitable network structures and the performances of function approximation for the two learning algorithms.