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

页码：2879 / 2889

页数：11

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