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
收藏
页码：262 / 270
页数：9
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