Robust Standard Gradient Descent Algorithm for ARX Models Using Aitken Acceleration Technique

被引：14

作者：

Chen, Jing ^{[1
]}

Gan, Min ^{[2
]}

Zhu, Quanmin ^{[3
]}

Narayan, Pritesh ^{[3
]}

Liu, Yanjun ^{[4
]}

机构：

[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Jiangsu, Peoples R China

[2] Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China

[3] Univ West England, Dept Engn Design & Math, Bristol BS16 1QY, Avon, England

[4] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China

来源：

IEEE TRANSACTIONS ON CYBERNETICS | 2022年 / 52卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Convergence; Acceleration; Mathematical model; Standards; Parameter estimation; Nonlinear equations; Computational modeling; Aitken acceleration technique; ARX model; convergence rate; parameter estimation; standard gradient descent (SGD) algorithm; LINEAR-SYSTEMS; IDENTIFICATION;

D O I：

10.1109/TCYB.2021.3063113

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration method is developed. Considering that the SGD algorithm has slow convergence rates and is sensitive to the step size, a robust and accelerative SGD (RA-SGD) algorithm is derived. This algorithm is based on the Aitken acceleration method, and its convergence rate is improved from linear convergence to at least quadratic convergence in general. Furthermore, the RA-SGD algorithm is always convergent with no limitation of the step size. Both the convergence analysis and the simulation examples demonstrate that the presented algorithm is effective.

引用

页码：9646 / 9655

页数：10

共 50 条

[1] Aitken-Based Stochastic Gradient Algorithm for ARX Models with Time Delay
Cheng Wang
Kaicheng Li
Circuits, Systems, and Signal Processing, 2019, 38 : 2863 - 2876
[2] Aitken-Based Stochastic Gradient Algorithm for ARX Models with Time Delay
Wang, Cheng
Li, Kaicheng
CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2019, 38 (06) : 2863 - 2876
[3] Parameter Identification of ARX Models Based on Modified Momentum Gradient Descent Algorithm
Tu, Quan
Rong, Yingjiao
Chen, Jing
COMPLEXITY, 2020, 2020
[4] Efficient gradient descent algorithm with anderson acceleration for separable nonlinear models
Chen, Guang-Yong
Lin, Xin
Xue, Peng
Gan, Min
NONLINEAR DYNAMICS, 2025, 113 (10) : 11371 - 11387
[5] Robust Identification of Switching Markov ARX Models Using EM Algorithm
Fan, Lei
Kodamana, Hariprasad
Huang, Biao
IFAC PAPERSONLINE, 2017, 50 (01): : 9772 - 9777
[6] A robust general normalised gradient descent algorithm
Mandic, Danilo P.
Obradoic, Dragan
Kuh, Anthony
2005 IEEE/SP 13th Workshop on Statistical Signal Processing (SSP), Vols 1 and 2, 2005, : 117 - 120
[7] An acceleration of gradient descent algorithm with backtracking for unconstrained optimization
Neculai Andrei
Numerical Algorithms, 2006, 42 : 63 - 73
[8] An acceleration of gradient descent algorithm with backtracking for unconstrained optimization
Andrei, Neculai
NUMERICAL ALGORITHMS, 2006, 42 (01) : 63 - 73
[9] Greedy Search Method for Separable Nonlinear Models Using Stage Aitken Gradient Descent and Least Squares Algorithms
Chen, Jing
Mao, Yawen
Gan, Min
Wang, Dongqing
Zhu, Quanmin
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2023, 68 (08) : 5044 - 5051
[10] Joint variable and variable projection algorithms for separable nonlinear models using Aitken acceleration technique
Cheng, Lianyuan
Chen, Jing
Rong, Yingjiao
INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2023, 42 (03) : 202 - 210

← 1 2 3 4 5 →