The Using of ANN at Design of Window Function Having Useful Spectral Parameters

Abstract

Abstract-The window functions are proposed structures at many application areas. The windows with helping two or more independent parameters show useful spectral characteristic. Kaiser window has been used at many areas because of the fact that it has good characteristic specification such as mainlobe width and the ripple ratio for various applications but Kaiser window having two parameters cannot control the sidelobe roll-off ratio. The ultraspherical window having three independent parameters has better sidelobe rolloff ratio than other windows in literature. At work, the coefficients of a new window function that has side-lobe roll-off ratio characteristic of ultraspherical window and Kaiser window's main-lobe width and ripple ratio were obtained by helping of artificial neural networks (ANN). 3000 data are used for training and testing of ANN. Sixty-five percentage of these data was used for learning of ANN and thirty-five percentage of that was used for testing process. It was observed that between the desired values and the obtained values using ANN were good agreements. Hence, a new window having better mainlobe width ripple ratio and side lobe roll-off ratio than other windows in spectral parameters at literature was modeled with ANN.