Multiple Delay Identification in Long Interconnects via LS-SVM Regression

This work presents a novel approach for the accurate estimation of multiple time-delays from the frequency response of a distributed system. The proposed approach is based on a powerful and flexible machine learning technique, namely, the least-square support vector machine (LS-SVM). The LS-SVM regression is used to construct a metamodel of the transfer function describing a generic linear time-invariant system in a delayed-rational form. Specifically, after some manipulation the LS-SVM model precisely identifies the dominant propagation delays of the original system. The essential steps and critical criteria for the delay identification procedure are carefully discussed throughout the paper. Once the system delays have been identified, the rational part of the metamodel expansion is then obtained by means of a progressive application of the conventional vector fitting algorithm. Numerical examples are presented to illustrate the feasibility and performance of the proposed technique and to compare its performances with what is provided by state-of-the-art techniques. The results clearly highlight the capability of the proposed approach to identify the dominant delays in distributed systems, thus allowing to construct compact delayed rational models.

Automated summary

This work presents a novel approach based on machine learning techniques for accurately estimating multiple time-delays in distributed systems. The method constructs a metamodel using LS-SVM regression, identifies delays, and obtains the rational part of the model using a vector fitting algorithm. Results show the capability of the proposed approach in identifying dominant delays and constructing compact delayed rational models.