Gradient-Based Recursive Identification Methods for Input Nonlinear Equation Error Closed-Loop Systems

被引：8
作者：
Shen, Bingbing [1 ]
Ding, Feng [1 ,2 ]
Alsaedi, Ahmed [2 ]
Hayat, Tasawar [2 ,3 ]
机构：
[1] Jiangnan Univ, Sch Internet Things Engn, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China
[2] King Abdulaziz Univ, Dept Math, Nonlinear Anal & Appl Math NAAM Res Grp, Jeddah 21589, Saudi Arabia
[3] Quaid I Azam Univ, Dept Math, Islamabad 44000, Pakistan
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2017年 / 36卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Stochastic gradient; Multi-innovation; Hierarchical identification; Nonlinear system; Closed-loop system; LEAST-SQUARES IDENTIFICATION; PARAMETER-ESTIMATION ALGORITHMS; AUXILIARY MODEL; HAMMERSTEIN SYSTEMS; FILTERING TECHNIQUE; DYNAMICAL-SYSTEMS; NEWTON ITERATION; FAULT-DETECTION; DELAY; CONVERGENCE;
D O I：
10.1007/s00034-016-0394-4
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The identification problem of closed-loop or feedback nonlinear systems is a hot topic. Based on the hierarchical identification principle, this paper presents a hierarchical stochastic gradient algorithm and a hierarchical multi-innovation stochastic gradient algorithm for feedback nonlinear systems. The simulation results show that the hierarchical multi-innovation stochastic gradient can more effectively estimate the parameters of the feedback nonlinear systems than the hierarchical stochastic gradient algorithm.
引用
收藏
页码：2166 / 2183
页数：18
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