Iterative identification algorithms for bilinear-in-parameter systems with autoregressive moving average noise

被引：56
作者：
Chen, Mengting [1 ]
Ding, Feng [1 ,2 ]
Xu, Ling [1 ]
Hayat, Tasawar [3 ,4 ]
Alsaedi, Ahmed [3 ]
机构：
[1] Jiangnan Univ, Sch Internet Things Engn, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266042, Peoples R China
[3] King Abdulaziz Univ, Dept Math, Nonlinear Anal & Appl Math NAAM Res Grp, Fac Sci, Jeddah 21589, Saudi Arabia
[4] Quaid I Azam Univ, Dept Math, Islamabad 44000, Pakistan
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2017年 / 354卷 / 17期
基金：
中国国家自然科学基金;
关键词：
NONLINEAR-SYSTEMS; MODEL; STATE; DESIGN;
D O I：
10.1016/j.jfranklin.2017.09.014
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 a bilinear-in-parameter system with autoregressive moving average noise. The basic idea is to use the over-parameterization to transform a system into a linear regressive model, and to present a gradient based and a least squares based iterative algorithms for identifying the system parameters. The numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：7885 / 7898
页数：14
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