Nonlinear Filtering Based on a Network with Gaussian Kernel Functions

Abstract

This paper presents a new method of nonlinear finetwork with Gaussian kernel functions. In practice, signal enhancement filters are usually adopted as a preprocessor of signal processing system. For this purpose, an approach of nonlinear filtering using a network with Gaussian kernel functions is proposed for the efficient enhancement of noisy signals. In this method, the condition for signal enhancement is obtained by using the phase space analysis of signal time series. Then, from this analysis, the structure of nonlinear filter is determined and a network with Gaussian kernel functions is trained in such a way of obtaining the clean signal. This procedure can be repeated to obtain the multilayer (or deep) structure of nonlinear filters. As a result, the proposed nonlinear filter has demonstrated significant merits in signal enhancement compared with other conventional preprocessing filters.