OPTIMAL HANKEL-NORM APPROXIMATION APPROACH TO MODEL-REDUCTION OF LARGE-SCALE MARKOV-CHAINS

被引：4

作者：

CHEN, GR

CHUI, CK

YU, YQ

机构：

[1] TEXAS A&M UNIV SYST,DEPT MATH,COLL STN,TX 77843

[2] TEXAS A&M UNIV SYST,DEPT ELECT ENGN,COLL STN,TX 77843

[3] UNIV CALIF IRVINE,DEPT ELECT & COMP ENGN,IRVINE,CA 92717

来源：

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE | 1992年 / 23卷 / 08期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1080/00207729208949383

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A model reduction problem of certain large-scale Markov chains under an optimal criterion for Hankel-norm approximation is discussed. The multi-dimensional Markov chain under investigation is assumed to have a finite-dimensional stationary state-transition matrix, which is first reformulated as a multi-input/multi-output (MIMO) linear time-invariant (LTI) stochastic system. Consequently, the resulting large-scale MIMO LTI stochastic system has a closed-form best approximant in the Hankel-norm from a specified class of stable lower-dimensional MIMO LTI systems.

引用

页码：1289 / 1297

页数：9

共 45 条

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