Efficient Balanced Truncation for RC and RLC Networks

Yuichi TANJI

doi:10.1587/transfun.E100.A.266

Efficient Balanced Truncation for RC and RLC Networks

Yuichi TANJI

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An efficient balanced truncation for RC and RLC networks is presented in this paper. To accelerate the balanced truncation, sparse structures of original networks are considered. As a result, Lyapunov equations, the solutions of which are necessary for making the transformation matrices, are efficiently solved, and the reduced order models are efficiently obtained. It is proven that reciprocity of original networks is preserved while applying the proposed method. Passivity of the reduced RC networks is also guaranteed. In the illustrative examples, we will show that the proposed method is compatible with PRIMA in efficiency and is more accurate than PRIMA.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.1 pp.266-274

Publication Date: 2017/01/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E100.A.266

Type of Manuscript: PAPER

Category: Circuit Theory

Authors

Yuichi TANJI
Kagawa University

Keyword

Lyapunov equations, balanced truncation, RC and RLC networks, signal/power integrity, model order reduction

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Yuichi TANJI, "Efficient Balanced Truncation for RC and RLC Networks" in IEICE TRANSACTIONS on Fundamentals, vol. E100-A, no. 1, pp. 266-274, January 2017, doi: 10.1587/transfun.E100.A.266.
Abstract: An efficient balanced truncation for RC and RLC networks is presented in this paper. To accelerate the balanced truncation, sparse structures of original networks are considered. As a result, Lyapunov equations, the solutions of which are necessary for making the transformation matrices, are efficiently solved, and the reduced order models are efficiently obtained. It is proven that reciprocity of original networks is preserved while applying the proposed method. Passivity of the reduced RC networks is also guaranteed. In the illustrative examples, we will show that the proposed method is compatible with PRIMA in efficiency and is more accurate than PRIMA.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.266/_p

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@ARTICLE{e100-a_1_266,
author={Yuichi TANJI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Efficient Balanced Truncation for RC and RLC Networks},
year={2017},
volume={E100-A},
number={1},
pages={266-274},
abstract={An efficient balanced truncation for RC and RLC networks is presented in this paper. To accelerate the balanced truncation, sparse structures of original networks are considered. As a result, Lyapunov equations, the solutions of which are necessary for making the transformation matrices, are efficiently solved, and the reduced order models are efficiently obtained. It is proven that reciprocity of original networks is preserved while applying the proposed method. Passivity of the reduced RC networks is also guaranteed. In the illustrative examples, we will show that the proposed method is compatible with PRIMA in efficiency and is more accurate than PRIMA.},
keywords={},
doi={10.1587/transfun.E100.A.266},
ISSN={1745-1337},
month={January},}

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TY - JOUR
TI - Efficient Balanced Truncation for RC and RLC Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 266
EP - 274
AU - Yuichi TANJI
PY - 2017
DO - 10.1587/transfun.E100.A.266
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2017
AB - An efficient balanced truncation for RC and RLC networks is presented in this paper. To accelerate the balanced truncation, sparse structures of original networks are considered. As a result, Lyapunov equations, the solutions of which are necessary for making the transformation matrices, are efficiently solved, and the reduced order models are efficiently obtained. It is proven that reciprocity of original networks is preserved while applying the proposed method. Passivity of the reduced RC networks is also guaranteed. In the illustrative examples, we will show that the proposed method is compatible with PRIMA in efficiency and is more accurate than PRIMA.
ER -