Asymptotic Parameter Estimation for a Class of Linear Stochastic Systems Using Kalman-Bucy Filtering

被引：6

作者：

Kan, Xiu ^{[2
]}

Shu, Huisheng ^{[1
]}

Che, Yan ^{[2
]}

机构：

[1] Donghua Univ, Sch Sci, Shanghai 200051, Peoples R China

[2] Donghua Univ, Sch Informat Sci & Technol, Shanghai 200051, Peoples R China

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2012年 / 2012卷

关键词：

MODEL;

D O I：

10.1155/2012/342705

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The asymptotic parameter estimation is investigated for a class of linear stochastic systems with unknown parameter theta : dX(t) = (theta alpha(t) + beta(t)X-t) dt + sigma(t)dW(t) . Continuous-time Kalman-Bucy linear filtering theory is first used to estimate the unknown parameter. based on Bayesian analysis. Then, some sufficient conditions on coefficients are given to analyze the asymptotic convergence of the estimator. Finally, the strong consistent property of the estimator is discussed by comparison theorem.

引用

页数：15

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