Single-input-single-output passive macromodeling via Positive Fractions Vector Fitting

Luciano De Tommasi; Dirk Deschrijver; Tom Dhaene

doi:10.1109/SPI.2008.4558387

Single-input-single-output passive macromodeling via Positive Fractions Vector Fitting

Luciano De Tommasi
, Dirk Deschrijver
, Tom Dhaene

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

Abstract

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.

Original language	English
Title of host publication	12th IEEE Workshop on Signal Propagation on Interconnects, SPI
DOIs	https://doi.org/10.1109/SPI.2008.4558387
Publication status	Published - 2008
Externally published	Yes
Event	12th IEEE Workshop on Signal Propagation on Interconnects, SPI - Avignon, France Duration: 12 May 2008 → 15 May 2008

Publication series

Name	12th IEEE Workshop on Signal Propagation on Interconnects, SPI

Conference

Conference	12th IEEE Workshop on Signal Propagation on Interconnects, SPI
Country/Territory	France
City	Avignon
Period	12/05/08 → 15/05/08

Access to Document

10.1109/SPI.2008.4558387

Cite this

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title = "Single-input-single-output passive macromodeling via Positive Fractions Vector Fitting",

abstract = "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.",

author = "Tommasi, \{Luciano De\} and Dirk Deschrijver and Tom Dhaene",

year = "2008",

doi = "10.1109/SPI.2008.4558387",

language = "English",

isbn = "9781424423187",

series = "12th IEEE Workshop on Signal Propagation on Interconnects, SPI",

booktitle = "12th IEEE Workshop on Signal Propagation on Interconnects, SPI",

note = "12th IEEE Workshop on Signal Propagation on Interconnects, SPI ; Conference date: 12-05-2008 Through 15-05-2008",

}

Tommasi, LD, Deschrijver, D & Dhaene, T 2008, Single-input-single-output passive macromodeling via Positive Fractions Vector Fitting. in 12th IEEE Workshop on Signal Propagation on Interconnects, SPI., 4558387, 12th IEEE Workshop on Signal Propagation on Interconnects, SPI, 12th IEEE Workshop on Signal Propagation on Interconnects, SPI, Avignon, France, 12/05/08. https://doi.org/10.1109/SPI.2008.4558387

Single-input-single-output passive macromodeling via Positive Fractions Vector Fitting. / Tommasi, Luciano De; Deschrijver, Dirk; Dhaene, Tom.
12th IEEE Workshop on Signal Propagation on Interconnects, SPI. 2008. 4558387 (12th IEEE Workshop on Signal Propagation on Interconnects, SPI).

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

TY - CHAP

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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.

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DO - 10.1109/SPI.2008.4558387

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SN - 9781424423187

T3 - 12th IEEE Workshop on Signal Propagation on Interconnects, SPI

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Y2 - 12 May 2008 through 15 May 2008

ER -

Single-input-single-output passive macromodeling via Positive Fractions Vector Fitting

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