LINEAR-SYSTEM IDENTIFICATION FROM NONSTATIONARY CROSS-SECTIONAL DATA

被引：29

作者：

GOODRICH, RL ^{[1
]}

CAINES, PE ^{[1
]}

机构：

[1] HARVARD UNIV,DIV APPL SCI,CAMBRIDGE,MA 02138

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 1979年 / 24卷 / 03期

关键词：

D O I：

10.1109/TAC.1979.1102037

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The identification of time-invariant linear stochastic systems from cross-sectional data on nonstationary system behavior is considered. A strong consistency and asymptotic normality result for maximum likelihood and prediction error estimates of the system parameters, system and measurement noise covariances, and the initial state covariance is proven. A new identifiability property for the system model is defined and appears in the set of conditions for this result The nonstationary stochastic realization (i.e., covariance factorization) theorem in [1] provides sufficient conditions for the identifiability property to hold. An application illustrating the use of a computer program implementing the identification method is presented. Copyright © 1979 by The Institute of Electrical and Electronics Engineers, Inc.

引用

页码：403 / 411

页数：9

共 21 条

[1]

AKAIKE H, 1977, SYSTEM IDENTIFICATIO

[2]

BYRNES C, 1977, 1976 AM RES CTR NASA, V7

[3] MAXIMUM LIKELIHOOD ESTIMATION OF PARAMETERS IN MULTIVARIATE GAUSSIAN STOCHASTIC-PROCESSES [J].