Fractional-Based Stochastic Gradient Algorithms for Time-Delayed ARX Models

被引：11
作者：
Xu, Tianyang [1 ,2 ]
Chen, Jing [1 ]
Pu, Yan [1 ]
Guo, Liuxiao [1 ]
机构：
[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Jiangsu, Peoples R China
[2] Nanjing Univ, Sch Management & Engn, Nanjing 210093, Peoples R China
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2022年 / 41卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Time delay; Fractional derivative; ARX model; Momentum method; Adaptive method; SYSTEM-IDENTIFICATION; DESCENT;
D O I：
10.1007/s00034-021-01874-8
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：
In this study, two fractional-based stochastic gradient (FSG) algorithms for time-delayed auto-regressive exogenous (ARX) models are proposed. By combining momentum and adaptive methods, a momentum-based FSG and an adaptive-based FSG algorithms are developed. These two FSG algorithms have faster convergence rates when compared with the stochastic gradient algorithm. The mechanism of the convergence is proved in theory. Furthermore, two simulated examples are presented to illustrate the efficiency of the new proposed algorithms.
引用
收藏
页码：1895 / 1912
页数：18
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