Abstract  In this paper, we deal with the problem of function approximation from a given set of input/output dat a. This problem consists of analyzing training examples, so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O da ta using Wavelet Neural Networks (WNN). This method is based on a new efficient method of optimizing the position of a si ngle function called mother wavelet of the WNN; it uses the objective output of WNN to move the position of wavelet single funct ion. This method calculates the error committed in every mother wavelet area using the real output of the WNN trying to con centrate more mother wavelets in those input region s where the error is bigger, thus attempting to homogenize the contribut ion to the error of every mother wavelet, this meth od improves the performance of the approximation system obtained, c ompared with other models derived from traditional algorithms.

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[18] Zhang Q. and Benveniste A., Wavelet Networks, IEEE Transactions on Neural Networks , vol. 3, no. 6, pp. 889-898, 1992. Mohammed Awad received his BSc degree in industrial automation engineering in 2000, from the Palestine Polytechnic University, and his PhD degree in 2005 from University of Granada, Spain. From February, 2006 till now he is an assistant professor in the Faculty of Engineering a nd Information Technology at the Arab American University, Palestine. From February, 2006 till now he is the chair of the Department of Computer Information Technology (CIT) at the Arab American University, Palestine. His current areas of research interest include artificial neural networks and evolutionary computation, function approximation using radial basis function neural networks, input variable selection and fuzzy and neuro-fuzzy system .