Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm

被引：16
|
作者：
Tu, Quan [1 ]
Rong, Yingjiao [2 ]
Chen, Jing [1 ]
机构：
[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Jiangsu, Peoples R China
[2] Sci & Technol Near Surface Detect Lab, Wuxi 214028, Jiangsu, Peoples R China
来源：
COMPLEXITY | 2020年 / 2020卷
基金：
中国国家自然科学基金;
关键词：
LEAST-SQUARES ALGORITHM; ITERATIVE ESTIMATION; SYSTEMS; RECOVERY; STATE;
D O I：
10.1155/2020/9537075
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The parameter estimation problem of the ARX model is studied in this paper. First, some traditional identification algorithms are briefly introduced, and then a new parameter estimation algorithm-the modified momentum gradient descent algorithm-is developed. Two gradient directions with their corresponding step sizes are derived in each iteration. Compared with the traditional parameter identification algorithms, the modified momentum gradient descent algorithm has a faster convergence rate. A simulation example shows that the proposed algorithm is effective.
引用
收藏
页数：11
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