Robust Gamma-filter Using Support Vector Machines

Abstract

This Letter presents a new approach to time-series modelling using the support vector machines (SVM). Although the g-filter can provide stability in several time-series models, the SVM is proposed here to provide robustness in the estimation of the g-filter coefficients. Examples in chaotic time-series prediction and channel equalization show the advantages of the joint SVM g-filter.