Parameter Estimation for Nonlinear Stochastic Model Using Generalized Entropy Optimization Principle

被引：0
作者：
Liu Yunlong [1 ]
Guo Lei [2 ]
Zhang Yumin [1 ]
机构：
[1] Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China
[2] Beihang Univ, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China
来源：
PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年
关键词：
Parameter Estimation; generalized entropy optimization principle; Parzen window; SYSTEMS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new type of parameter estimation method has been proposed for a class of nonlinear stochastic model with non-Gaussian disturbance The Parzen window method was first used to estimate the density function of the sampled data and then the generalized entropy optimization principle was used to estimate the unknown parameters. No matter what distribution the noise obeys to, Gaussian or non-Gaussian, unbiased parameter estimated values can be obtained. The simulation results show the effectiveness of the proposed approaches.
引用
收藏
页码：1901 / 1905
页数：5
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