Nonlinear model order reduction based on tensor Kronecker product expansion with Arnoldi process

被引：7

作者：

Jiang, Yao-Lin ^{[1
]}

Chen, Hai-Bao ^{[1
]}

Qi, Zhen-Zhong ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Shaanxi, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2016年 / 353卷 / 14期

关键词：

TRANSMISSION-LINES; FAST SIMULATION; SYSTEMS; APPROXIMATION;

D O I：

10.1016/j.jfranklin.2016.06.023

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present a model order reduction (MOR) method for large nonlinear input output systems based on tensor Kronecker product expansion with Arnoldi process. We first approximate the nonlinear system with a quadratic form at single-point expansion, and then use a tensor Kronecker product analysis to it. Constructing the projection matrix through solving a linear equation, a reduced quadratic system is produced, which can match the first several expansion coefficients of the original output. What is more, it can preserve the stability and passivity under some certain conditions as well. The error estimation is also well discussed. Finally, the robust behavior of our MOR method is successfully illustrated via two numerical examples. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：3641 / 3655

页数：15

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