An adaptive-order rational Arnoldi method for model-order reductions of linear time-invariant systems

By Herng-Jer Lee, Chia-Chi Chu, Wu-Shiung Feng. 2006

53 citations recorded by the source corpus.

Abstract This work proposes a model reduction method, the adaptive-order rational Arnoldi (AORA) method, to be applied to large-scale linear systems. It is based on an extension of the classical multi-point Pade approximation (or the so-called multi-point moment matching), using the rational Arnoldi iteration approach. Given a set of predetermined expansion points, an exact expression for the error between the output moment of the original system and that of the reduced-order system, related to each expansion point, is derived first. In each iteration of the proposed adaptive-order rational Arnoldi algorithm, the expansion frequency corresponding to the maximum output moment error will be chosen. Hence, the corresponding reduced-order model yields the greatest improvement in output moments among all reduced-order models of the same order. A detailed theoretical study is described. The proposed method is very appropriate for large-scale electronic systems, including VLSI interconnect models and digital filter designs. Several examples are considered to demonstrate the effectiveness and efficiency of the proposed method.

Subfields: Electrical and Electronic Engineering, Numerical Analysis, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/19c5e4dcf05c2e236982f3e60e5c83fb25f54c4b

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In Electrical and Electronic Engineering

  1. Peter Benner
  2. Ulrike Baur
  3. Lihong Feng

In Numerical Analysis

  1. Peter Benner
  2. Ulrike Baur
  3. Lihong Feng

In Statistical and Nonlinear Physics

  1. Peter Benner
  2. Ulrike Baur
  3. Lihong Feng

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