Abstract
This paper presents an original architecture of Wavelet Neural Network based on multi Wavelets activation function and uses a selection method to determine a set of best wavelets whose centers and dilation parameters are used as initial values for subsequent training library WNN for one dimension and two dimensions function approximation. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network.