The paper deals with the numerical validation, performance evaluation and robustness assessment of a procedure for the direct identification of passive transfer matrices based on convex programming. Validation is pursued by producing data sets from lumped multiport systems with random parameters (passive, non passive, and possibly affected by random noise), then evaluating the identification ability of the considered method. Results demonstrate how the considered approach satisfactorily covers the passive identification of a large class of data sets, even in presence of significant passivity violations or noise flawed data, at average accuracies comparable with non passive identifications obtained with standard Vector Fitting.
Numerical validation of a procedure for direct identification of passive linear multiport with convex programming
DE MAGISTRIS, MASSIMILIANO;
2010-01-01
Abstract
The paper deals with the numerical validation, performance evaluation and robustness assessment of a procedure for the direct identification of passive transfer matrices based on convex programming. Validation is pursued by producing data sets from lumped multiport systems with random parameters (passive, non passive, and possibly affected by random noise), then evaluating the identification ability of the considered method. Results demonstrate how the considered approach satisfactorily covers the passive identification of a large class of data sets, even in presence of significant passivity violations or noise flawed data, at average accuracies comparable with non passive identifications obtained with standard Vector Fitting.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
