Maximum Likelihood Iterative Algorithm for Hammerstein Systems with Hard Nonlinearities

被引：4
|
作者：
Pu, Yan [1 ]
Yang, Yongqing [1 ]
Chen, Jing [1 ]
机构：
[1] Jiangnan Univ, Sch Internet Things Engn, Sch Sci, Wuxi 214122, Jiangsu, Peoples R China
来源：
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2020年 / 18卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Gradient search; Hammerstein system; key term separation; maximum likelihood; saturation nonlinearity; IDENTIFICATION METHODS; LINEAR-SYSTEMS; SATURATION; MODELS;
D O I：
10.1007/s12555-019-0799-5
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we consider several iterative algorithms for Hammerstein systems with hard nonlinearities. The Hammerstein system is first simplified as a polynomial identification model through the key term separation technique, and then the parameters are estimated by using the maximum likelihood (ML) based gradient-based iterative algorithm. Furthermore, an ML least squares auxiliary variable algorithm and an ML bias compensation gradient-based iterative algorithm are developed to identify the saturation system with colored noise. Simulation results are included to illustrate the effectiveness of the proposed algorithms.
引用
收藏
页码：2879 / 2889
页数：11
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