Abstract

LUZARDO, Ender; PAREDES, José L  and  RAMIREZ, Jaime. A Kernel based approach for classification of electromagnetic interference signals. Rev. Téc. Ing. Univ. Zulia [online]. 2007, vol.30, n.1, pp.33-45. ISSN 0254-0770.

Abstract This paper introduces Electromagnetic Interference signal classification methods for signals obtained on ribbon cables with different crosstalk configurations. The proposed method comprises two stages. The first one is a preprocessing stage that applies either Principal Components Analysis (PCA), Kernel Principal Components Analysis (KPCA) or Independent Components Analysis (ICA) to reduce the data dimension and, at the same time, to obtain the most relevant information from the raw data. The second stage, the classification one, uses Support Vector Machine (SVM) to classify the kind of electromagnetic coupling. We compare the performance of the different classification structures obtained by combining a pre-processing method with SVM, namely PCA+SVM, KPCA+SVM, ICA+SVM as well as SVM in the time domain

Keywords : Electromagnetic interference; principal components analysis; Kernel principal components analysis; independent components analysis; support vector machine.