Smoothing irregularly sampled signals by convolutional RBF networks

Abstract

Convolutional radial basis function (RBF) networks are introduced for smoothing out irregularly sampled signals. The proposed technique involves training an RBF network and then convolving it with a Gaussian smoothing kernel in an analytical manner. Since the convolution results in an analytic form, the computation necessary for numerical convolution is avoided. Convolutional RBF networks need training only once, do not depend on any particular details of the training methods used, and different degrees of smoothing are immediately available.