Recursive Extended Least Squares Parameter Estimation for Wiener Nonlinear Systems with Moving Average Noises

被引：0
|
作者：
Yuanbiao Hu
Baolin Liu
Qin Zhou
Chun Yang
机构：
[1] China University of Geosciences,Key Laboratory on Deep GeoDrilling Technology of the Ministry of Land and Resources
来源：
Circuits, Systems, and Signal Processing | 2014年 / 33卷
关键词：
Least squares; Parameter estimation; Recursive identification; Nonlinear system;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Many control algorithms are based on the mathematical models of dynamic systems. System identification is used to determine the structures and parameters of dynamic systems. Some identification algorithms (e.g., the least squares algorithm) can be applied to estimate the parameters of linear regressive systems or linear-parameter systems with white noise disturbances. This paper derives two recursive extended least squares parameter estimation algorithms for Wiener nonlinear systems with moving average noises based on over-parameterization models. The simulation results indicate that the proposed algorithms are effective.
引用
收藏
页码：655 / 664
页数：9
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