Name
Abstract | ||
|---|---|---|
The passive and sparse reduced-order modeling of a RLC network is presented, where eigenvalues and eigenvectors of the original network are used, and thus the obtained macromodel is more accurate than that provided by the Krylov subspace methods or TBR procedures for a class of circuits. Furthermore, the proposed method is applied to low pass filtering of a reduced-order model produced by these methods without breaking the passivity condition. Therefore, the proposed eigenspace method is not only a reduced-order macromodeling method, but also is embedded in other methods enhancing their performances. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/ietfec/e91-a.9.2419 | IEICE Transactions |
Keywords | Field | DocType |
tbr procedure,sparse reduced-order modeling,reduced-order macromodeling method,reduced-order model,passive reduced-order interconnect modeling,krylov subspace method,proposed eigenspace method,rlc network,eigenspace method,passivity condition,original network,eigenvalues and eigenvectors,eigen decomposition,low pass filter | Passivity,Krylov subspace,Model order reduction,Filter (signal processing),Theoretical computer science,Low-pass filter,Eigendecomposition of a matrix,RLC circuit,Mathematics,Eigenvalues and eigenvectors | Journal |
Volume | Issue | ISSN |
E91-A | 9 | 0916-8508 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yuichi tanji | 1 | 32 | 12.18 |