A Novel EM Identification Method for Hammerstein Systems With Missing Output Data

被引：131

作者：

Wang, Dongqing ^{[1
]}

Zhang, Shuo ^{[1
]}

Gan, Min ^{[1
]}

Qiu, Jianlong ^{[2
]}

机构：

[1] Qingdao Univ, Coll Elect Engn, Qingdao 266071, Peoples R China

[2] Linyi Univ, Sch Automat & Elect Engn, Linyi 276000, Shandong, Peoples R China

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2020年 / 16卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Data models; Informatics; Estimation; Computational modeling; Data systems; Gallium nitride; Nonlinear systems; Data loss; expectation maximization (EM) algorithm; Hammerstein systems; parameter identification; MULTIVARIABLE NONLINEAR-SYSTEMS; PARAMETER-ESTIMATION ALGORITHM; RECURSIVE-IDENTIFICATION; ITERATIVE ALGORITHM; DYNAMIC-SYSTEMS; MODEL RECOVERY; ERROR SYSTEMS; DEAD-ZONE; CONVERGENCE; DIAGNOSIS;

D O I：

10.1109/TII.2019.2931792

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 article concerns a novel auxiliary-model-based expectation maximization (EM) estimation method for Hammerstein systems with data loss by extending the EM method to estimate models with multiple parameter vectors. The novel EM method relaxes the requirements on an autoregression model with one parameter vector, interactively maximizes the expectation over multiple parameter vectors in a more general model, and uses the output of an auxiliary model to substitute the missing outputs in the information vector in iteration processes. A numerical simulation is employed to demonstrate the effectiveness of the proposed novel EM method.

引用

页码：2500 / 2508

页数：9

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