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

页码：5023 / 5048

页数：26

共 50 条

[1] Filtering-Based Maximum Likelihood Gradient Iterative Estimation Algorithm for Bilinear Systems with Autoregressive Moving Average Noise
Meihang Li
Ximei Liu
Feng Ding
[J]. Circuits, Systems, and Signal Processing, 2018, 37 : 5023 - 5048
[2] The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving, average noise
Li, Meihang
Liu, Ximei
Ding, Feng
[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2017, 354 (12): : 4861 - 4881
[3] The filtering-based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle
Li, Meihang
Liu, Ximei
Ding, Feng
[J]. INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2019, 33 (07) : 1189 - 1211
[4] Data filtering based maximum likelihood extended gradient method for multivariable systems with autoregressive moving average noise
Chen, Feiyan
Ding, Feng
Xu, Ling
Hayat, Tasawar
[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2018, 355 (07): : 3381 - 3398
[5] Filtering-based recursive least-squares identification algorithm for controlled autoregressive moving average systems using the maximum likelihood principle
Li, Junhong
Ding, Feng
[J]. JOURNAL OF VIBRATION AND CONTROL, 2015, 21 (15) : 3098 - 3106
[6] The Gradient-Based Iterative Estimation Algorithms for Bilinear Systems with Autoregressive Noise
Meihang Li
Ximei Liu
Feng Ding
[J]. Circuits, Systems, and Signal Processing, 2017, 36 : 4541 - 4568
[7] The Gradient-Based Iterative Estimation Algorithms for Bilinear Systems with Autoregressive Noise
Li, Meihang
Liu, Ximei
Ding, Feng
[J]. CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2017, 36 (11) : 4541 - 4568
[8] Maximum likelihood least squares identification for systems with autoregressive moving average noise
Wang, Wei
Ding, Feng
Dai, Jiyang
[J]. APPLIED MATHEMATICAL MODELLING, 2012, 36 (05) : 1842 - 1853
[9] Iterative identification algorithms for bilinear-in-parameter systems with autoregressive moving average noise
Chen, Mengting
Ding, Feng
Xu, Ling
Hayat, Tasawar
Alsaedi, Ahmed
[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2017, 354 (17): : 7885 - 7898
[10] Data filtering based multi-innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle
Chen, Feiyan
Ding, Feng
Alsaedi, Ahmed
Hayat, Tasawar
[J]. MATHEMATICS AND COMPUTERS IN SIMULATION, 2017, 132 : 53 - 67

← 1 2 3 4 5 →