The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving, average noise

被引：61

作者：

Li, Meihang ^{[1
]}

Liu, Ximei ^{[1
]}

Ding, Feng ^{[1
,2
]}

机构：

[1] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266042, Peoples R China

[2] King Abdulaziz Univ, Fac Sci, Dept Math, Nonlinear Anal & Appl Math NAAM Res Grp, Jeddah 21589, Saudi Arabia

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2017年 / 354卷 / 12期

基金：

中国国家自然科学基金;

关键词：

PARAMETER-ESTIMATION ALGORITHMS; IDENTIFICATION ALGORITHM;

D O I：

10.1016/j.jfranklin.2017.05.017

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Maximum likelihood methods are significant for parameter estimation and system modeling. This paper gives the input-output representation of a bilinear system through eliminating the state variables in it, and derives a maximum likelihood least squares based iterative for identifying the parameters of bilinear systems with colored noises by using the maximum likelihood principle. A least squares based iterative (LSI) algorithm is presented for comparison. It is proved that the maximum of the likelihood function is equivalent to minimize the least squares cost function. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems and the maximum likelihood LSI algorithm is more accurate than the LSI algorithm. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：4861 / 4881

页数：21

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