Identification of Errors-in-Variables Systems with General Nonlinear Output Observations and with ARMA Observation Noises

被引：4

作者：

Song, Qijiang ^{[1
]}

Huang, Zhiyong ^{[1
]}

机构：

[1] Renmin Univ China, Sch Math, Beijing 100872, Peoples R China

来源：

JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY | 2020年 / 33卷 / 01期

基金：

中国国家自然科学基金;

关键词：

ARMA noise; alpha-mixing; binary sensor; errors-in-variables; nonlinear observation; recursive estimate; stochastic approximation (SA); strongly consistent; system identification; RECURSIVE-IDENTIFICATION; WIENER;

D O I：

10.1007/s11424-020-9009-z

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper concerns the identification problem of scalar errors-in-variables (EIV) systems with general nonlinear output observations and ARMA observation noises. Under independent and identically distributed (i.i.d.) Gaussian inputs with unknown variance, recursive algorithms for estimating the parameters of the EIV systems are presented. For general nonlinear observations, conditions on the system are imposed to guarantee the almost sure convergence of the estimates. A simulation example is included to justify the theoretical results.

引用

页码：1 / 14

页数：14

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