Biased compensation recursive least squares-based threshold algorithm for time-delay rational models via redundant rule

被引：0

作者：

Jing Chen

Quan Min Zhu

Juan Li

Yan Jun Liu

机构：

[1] Jiangnan University,School of Science

[2] University of the West of England,Department of Engineering Design and Mathematics

[3] Qingdao Agricultural University,College of Mechanical and Electrical Engineering

[4] Jiangnan University,School of Internet of Things Engineering

来源：

Nonlinear Dynamics | 2018年 / 91卷

关键词：

Parameter estimation; Recursive least squares; Biased compensation; Time delay; Rational model;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper develops a biased compensation recursive least squares-based threshold algorithm for a time-delay rational model. The time-delay rational model is transformed into an augmented model by using the redundant rule, and then, a recursive least squares algorithm is proposed to estimate the parameters of the augmented model. Since the output of the augmented model is correlated with the noise, a biased compensation method is derived to eliminate the bias of the parameter estimates. Furthermore, based on the structures of the augmented model parameter vector and the rational model parameter vector, the unknown time delay can be computed by using a threshold given in prior. A simulated example is used to illustrate the efficiency of the proposed algorithm.

引用

页码：797 / 807

页数：10