Filtering-Based Maximum Likelihood Gradient Iterative Estimation Algorithm for Bilinear Systems with Autoregressive Moving Average Noise

被引：42
|
作者：
Li, Meihang [1 ]
Liu, Ximei [1 ]
Ding, Feng [1 ,2 ]
机构：
[1] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266042, Peoples R China
[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2018年 / 37卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Iterative identification; Gradient search; Maximum likelihood; Bilinear system; PARAMETER-ESTIMATION ALGORITHM; RECURSIVE LEAST-SQUARES; DYNAMICAL-SYSTEMS; IDENTIFICATION; EQUATIONS; STATE; STRATEGY; MODEL;
D O I：
10.1007/s00034-018-0800-1
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：
This paper combines the maximum likelihood principle with the data filtering technique for parameter estimation of bilinear systems with autoregressive moving average noise. We give the input-output representation of the bilinear systems through eliminating the state variables in the model. Based on the obtained model, we use an estimated noise transfer function to filter the input-output data and derive a filtering-based maximum likelihood gradient iterative algorithm for identifying the parameters of bilinear systems with colored noises. A gradient-based iterative algorithm is given for comparison. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems.
引用
收藏
页码：5023 / 5048
页数：26
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