Loewner Matrix Macromodeling for Y-Parameter Data With a Priori $\textbf {D}$ Matrix Extraction

Kabir, Muhammad Nomani; Khazaka, Roni

doi:10.1109/tmtt.2016.2608771

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Review Of the Lm Algorithm1

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Cited by 13 publications

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“…where the DSS matrices are constructed as: (10) where X n ∈ R k×n and Y n ∈ R k×n are constructed from the first n columns of X and Y of Equation ( 8) respectively [4]. If needed, the value of D is extracted as described in [21]. The next section discusses the RKFIT algorithm.…”

Section: Review Of the Lm Algorithmmentioning

confidence: 99%

Macromodeling High-Speed Circuit Data Using Rational Krylov Fitting Method

Sahouli

Dounavis

2021

Energies

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This paper presents the modeling of high speed distributed networks characterized by S-parameters frequency data using the rational Krylov fitting (RKFIT) algorithm. Numerical examples illustrate the effectiveness of the method to compute stable rational approximation that fit given S-parameters data. In addition, it is shown that RKFIT has some advantages when compared to the well-established Vector Fitting (VF) method, such as more accurate fitting, less dependence on the choice of the initial poles of the algorithm, and faster convergence. Numerical examples are implemented using RKFIT and the results are compared with VF and the Loewner Matrix (LM) algorithm.

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Section: Review Of the Lm Algorithmmentioning

confidence: 99%