Parameter estimation for partially observed nonlinear stochastic system

被引：1

作者：

Wei, Chao ^{[1
]}

He, Chaobing ^{[1
]}

机构：

[1] Anyang Normal Univ, Sch Math & Stat, Anyang 455000, Peoples R China

来源：

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS | 2019年 / 10卷 / 02期

关键词：

nonlinear stochastic system; state estimation equation; parameter estimation; strong consistency; computing science; mathematics; extended Kalman filtering; suboptimal estimation; ASYMPTOTIC-BEHAVIOR; DIFFUSION; MODELS;

D O I：

10.1504/IJCSM.2019.098739

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper is concerned with the parameter estimation problem for partially observed nonlinear stochastic system. The suboptimal estimation of the state is obtained by constructing the extended Kalman filtering equation. The likelihood function is provided based on state estimation equation. The strong consistency of the estimator is proved by applying maximal inequality for martingales, Borel-Cantelli lemma and uniform ergodic theorem. An example is provided to verify the effectiveness of the method.

引用

页码：150 / 159

页数：10

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