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Passivity-Based Sample Selection and Adaptive Vector Fitting Algorithm for Pole-Residue Modeling of Sparse Frequency-Domain Data

机译：基于无源性的样本选择和自适应矢量拟合算法用于稀疏频域数据的极点残差建模

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Dirk Deschrijver; Tom Dhaene;
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年度 2013
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1. Vector Fitting Based Adaptive Frequency Sampling for Compact Model Extraction on HPC Systems [J] . Ciuprina G., Ioan D., Lazar I. A., Magnetics, IEEE Transactions on . 2012,第2期

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2. Review and comparison of frequency-domain curve-fitting techniques: Vector fitting, frequency-partitioning fitting, matrix pencil method and loewner matrix [J] . Salarieh B., De Silva H. M. J. Electric power systems research . 2021,第Jula期

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3. Algorithms and Data Structures for Matrix-Free Finite Element Operators with MPI-Parallel Sparse Multi-Vectors [J] . DENIS DAVYDOV, MARTIN KRONBICHLER ACM Transactions on Parallel Computing . 2020,第3期

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4. Passivity-based sample selection and adaptive vector fitting algorithm for pole-residue modeling of sparse frequency-domain data [C] . Deschrijver, D., Dhaene, . 2004

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5. Improved EM-Type Algorithms for Fitting Marginal Zero-Inflated Regression Models to Clustered Data with Excess Zeros [D] . Benesi, Tawanda. 2020

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7. Sparse Precision Matrix Selection for Fitting Gaussian Random Field Models to Large Data Sets [O] . Tajbakhsh, Sam Davanloo, Aybat, Necdet Serhat, Del Castillo, Enrique 2016

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Passivity-Based Sample Selection and Adaptive Vector Fitting Algorithm for Pole-Residue Modeling of Sparse Frequency-Domain Data

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