Recursive Subspace identification for time-varying continuous-time stochastic systems via distribution theory

被引：0
|
作者：
Yu, Miao [1 ]
Liu, Jianchang [2 ,3 ]
Wang, Honghai [2 ,3 ]
机构：
[1] Northeastern Univ Qinhuangdao, Sch Control Engn, Qinhuangdao 066004, Hebei, Peoples R China
[2] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
[3] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China
来源：
2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
Distribution theory; Recursive subspace identification; Time-varying system; Stochastic system; MODEL IDENTIFICATION; PREDICTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a recursive subspace identification method for time-varying continuous-time stochastic systems based on distribution theory. By using the random distribution theory, the time-derivative of stochastic processes is described and the input-output matrix equation is obtained. Moreover, we reduce the storage cost and computation burden by keeping the size of input-output data to be constant. Further, the system model is obtained. The simulation results show the validity and accuracy of the proposed method.
引用
收藏
页码：1310 / 1313
页数：4
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