Recursive search-based identification algorithms for the exponential autoregressive time series model with coloured noise

被引：6

作者：

Xu, Huan ^{[1
]}

Ding, Feng ^{[1
,2
]}

Yang, Erfu ^{[3
]}

机构：

[1] Jiangnan Univ, Sch Internet Things Engn, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Jiangsu, Peoples R China

[2] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266061, Peoples R China

[3] Univ Strathclyde, Strathclyde Space Inst, Dept Design Mfg & Engn Management, Space Mechatron Syst Technol Lab, Glasgow G1 1XJ, Lanark, Scotland

来源：

IET CONTROL THEORY AND APPLICATIONS | 2020年 / 14卷 / 02期

基金：

中国国家自然科学基金;

关键词：

gradient methods; recursive estimation; time series; parameter estimation; least squares approximations; stochastic processes; autoregressive moving average processes; MI-ESG algorithm; parameter estimation accuracy; appropriate innovation length; forgetting factor; unknown parameters; ExpARMA model; recursive search-based identification algorithms; exponential autoregressive time series model; coloured noise; recursive parameter estimation problems; nonlinear exponential autoregressive model; average noise; gradient search; extended stochastic gradient algorithm; optimal step-size; multiinnovation identification theory; multiinnovation ESG algorithm; PARAMETER-ESTIMATION ALGORITHM; STATE-SPACE SYSTEM; NONLINEAR-SYSTEMS; DESIGN; SPEED;

D O I：

10.1049/iet-cta.2019.0429

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 study focuses on the recursive parameter estimation problems for the non-linear exponential autoregressive model with moving average noise (the ExpARMA model for short). By means of the gradient search, an extended stochastic gradient (ESG) algorithm is derived. Considering the difficulty of determining the step-size in the ESG algorithm, a numerical approach is proposed to obtain the optimal step-size. In order to improve the parameter estimation accuracy, the authors employ the multi-innovation identification theory to develop a multi-innovation ESG (MI-ESG) algorithm for the ExpARMA model. Introducing a forgetting factor into the MI-ESG algorithm, the parameter estimation accuracy can be further improved. With an appropriate innovation length and forgetting factor, the variant of the MI-ESG algorithm is effective to identify all the unknown parameters of the ExpARMA model. A simulation example is provided to test the proposed algorithms.

引用

页码：262 / 270

页数：9

共 69 条

[41] Feedback stabilisation of time-delay nonlinear systems with continuous time-varying output function [J].

Sun, Zong-Yao ;

Zhang, Di ;

Meng, Qinghua ;

Chen, Chih-Chiang .

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2019, 50 (02) :244-255

[42] SPECIFICATION, ESTIMATION, AND EVALUATION OF SMOOTH TRANSITION AUTOREGRESSIVE MODELS [J].

TERASVIRTA, T .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1994, 89 (425) :208-218

[43] Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems [J].

Wan, Lijuan ;

Liu, Ximei ;

Ding, Feng ;

Chen, Chunping .

MATHEMATICS, 2019, 7 (07)

[44] Decomposition- and Gradient-Based Iterative Identification Algorithms for Multivariable Systems Using the Multi-innovation Theory [J].

Wan, Lijuan ;

Ding, Feng .

CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2019, 38 (07) :2971-2991

[45] A T-wave alternans assessment method based on least squares curve fitting technique [J].

Wan, Xiangkui ;

Li, Yan ;

Xia, Chong ;

Wu, Minghu ;

Liang, Jin ;

Wang, Na .

MEASUREMENT, 2016, 86 :93-100

[46] Novel Method for Identifying Fault Location of Mixed Lines [J].

Wang, Lei ;

Liu, Hui ;

Le Van Dai ;

Liu, Yuwei .

ENERGIES, 2018, 11 (06)

[47] On disturbance rejection in magnetic levitation [J].

Wei, Wei ;

Xue, Wenchao ;

Li, Donghai .

CONTROL ENGINEERING PRACTICE, 2019, 82 :24-35

[48] A Capacity Configuration Control Strategy to Alleviate Power Fluctuation of Hybrid Energy Storage System Based on Improved Particle Swarm Optimization [J].

Wu, Tiezhou ;

Shi, Xiao ;

Liao, Li ;

Zhou, Chuanjian ;

Zhou, Hang ;

Su, Yuehong .

ENERGIES, 2019, 12 (04)

[49] Fitting the exponential autoregressive model through recursive search [J].

Xu, Huan ;

Wan, Lijuan ;

Ding, Feng ;

Alsaedi, Ahmed ;

Hayat, Tasawar .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2019, 356 (11) :5801-5818

[50] On some parameter estimation algorithms for the nonlinear exponential autoregressive model [J].

Xu, Huan ;

Ding, Feng ;

Sheng, Jie .

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2019, 33 (06) :999-1015

← 1 2 3 4 5 6 7 →