Wajdi Bellil and Chokri Ben Amar and Adel M. Alimi Comparison between Beta Wavelets Neural Networks, RBF Neural Networks and Polynomial Approximation for 1D, 2DFunctions Approximation
189 - 194
2008
2
1
International Journal of Computer, Electrical, Automation, Control and Information Engineering http://waset.org/publications/2132
http://waset.org/publications/13
World Academy of Science, Engineering and Technology
This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1D and 2D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1D and 2D functions approximation. Our purpose is to approximate an unknown function f Rn R from scattered samples (xi; y f(xi)) i1....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.
International Science Index 13, 2008