Data filtering based maximum likelihood forgetting stochastic gradient identification algorithm for Box-Jenkins systems

被引：0

作者：

Li, Junhong ^{[1
]}

Yang, Yi ^{[1
]}

Mao, Jingfeng ^{[1
]}

Li, Chen ^{[1
]}

Zhang, Qing ^{[1
]}

机构：

[1] Nantong Univ, Sch Elect Engn, Nantong 226019, Peoples R China

来源：

PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年

关键词：

Recursive identification; Stochastic gradient; Maximum likelihood; Data filtering; Parameter estimation; ERRORS-IN-VARIABLES; LEAST-SQUARES; PARAMETER-IDENTIFICATION; RECURSIVE-IDENTIFICATION; HAMMERSTEIN SYSTEMS; MODELS;

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：

This paper considers the identification problems of Box-Jenkins systems based on the data filtering technique and maximum likelihood principle. After using the noise polynomial to filter the input and output data, two identification models are obtained. Then a maximum likelihood stochastic gradient algorithm and a stochastic gradient estimation algorithm are derived to interactively estimate the parameters of the two identification models. The simulation results show that the proposed algorithms can effectively estimate the parameters of Box-Jenkins systems.

引用

页码：1416 / 1420

页数：5

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