TY - GEN
T1 - Single-input-single-output passive macromodeling via Positive Fractions Vector Fitting
AU - Tommasi, Luciano De
AU - Deschrijver, Dirk
AU - Dhaene, Tom
PY - 2008
Y1 - 2008
N2 - This paper introduces a constrained Vector Fitting algorithm which can directly identify a passive driving point function (impedance or admittance) from frequency domain data. The proposed Positive Fractions Vector Fitting (PFVF) algorithm formulates the residue identification step as a convex programming problem, while the pole identification step follows the unaltered standard Vector Fitting procedure. A further extension to multi-input-multi- output functions is possible and is under investigation.
AB - This paper introduces a constrained Vector Fitting algorithm which can directly identify a passive driving point function (impedance or admittance) from frequency domain data. The proposed Positive Fractions Vector Fitting (PFVF) algorithm formulates the residue identification step as a convex programming problem, while the pole identification step follows the unaltered standard Vector Fitting procedure. A further extension to multi-input-multi- output functions is possible and is under investigation.
UR - https://www.scopus.com/pages/publications/51849141060
U2 - 10.1109/SPI.2008.4558387
DO - 10.1109/SPI.2008.4558387
M3 - Conference proceeding
AN - SCOPUS:51849141060
SN - 9781424423187
T3 - 12th IEEE Workshop on Signal Propagation on Interconnects, SPI
BT - 12th IEEE Workshop on Signal Propagation on Interconnects, SPI
T2 - 12th IEEE Workshop on Signal Propagation on Interconnects, SPI
Y2 - 12 May 2008 through 15 May 2008
ER -