Approximating i/o data using wavelet neural networks: control the position of mother wavelet

In this paper, we deal with the problem of function approximation from a given set of input/output data. 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 data using Wavelet Neural Networks (WNN). This method is based on a new efficient method of optimizing the position of a single function called mother wavelet of the WNN; it uses the objective output of WNN to move the position of wavelet single function. This method calculates the error committed in every mother wavelet area using the real output of the WNN trying to concentrate more mother wavelets in those input regions where the error is bigger, thus attempting to homogenize the contribution to the error of every mother wavelet, this method improves the performance of the approximation system obtained, compared with other models derived from traditional algorithms.