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

作     者：SONG Qijiang HUANG Zhiyong SONG Qijiang;HUANG Zhiyong

作者机构：School of MathematicsRenmin University of ChinaBeijing 100872China 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2020年第33卷第1期

页      面：1-14页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by the National Natural Science Foundation of China under Grant No.11571362 

主　　题：ARMA noise α-mixing binary sensor errors-in-variables nonlinear observation recursive estimate stochastic approximation(SA) strongly consistent system identification 

摘      要：This paper concerns the identification problem of scalar errors-in-variables(EIV)systems with general nonlinear output observations and ARMA observation *** independent and identically distributed(i.i.d.)Gaussian inputs with unknown variance,recursive algorithms for estimating the parameters of the EIV systems are *** 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.