TY - JFULL
AU - Wajdi Bellil and Chokri Ben Amar and Adel M. Alimi
PY - 2008/2/
TI - Comparison between Beta Wavelets Neural Networks, RBF Neural Networks and Polynomial Approximation for 1D, 2DFunctions Approximation
T2 - International Journal of Aerospace and Mechanical Engineering
SP - 188
EP - 194
VL - 2
SN - 1307-6892
UR - https://publications.waset.org/pdf/2132
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 13, 2008
N2 - This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn - R from scattered samples (xi; y = f(xi)) i=1....n, where first, we have little a priori knowledge on the unknown function f: it lives in some infinite dimensional smooth function space and second the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate Ôêºf as an approximation of the function f. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network.
ER -