Machine Learning Applied to the Blind Identification of Multiple Delays in Distributed Systems

Abstract

This paper focuses on the application of the Least-Square Support Vector Machine (LS-SVM) regression for the modeling of frequency responses of complex interconnect structures. The goal is to obtain a delayed-rational model (DRM) for the structure accounting for multiple time-delays generated by wave propagation and reflections along the channel. A novel approach for the time-delays estimation based on the LS-SVM regression is introduced. The delays are estimated using the dual space formulation of the LS-SVM with an ad-hoc kernel that considers a possible delay interval. The results highlight the lower order of DRMs obtained using the delays identified through this method when companng to the vector fitting approach by applying it to a high-speed cable link.