A new filter-based stochastic gradient algorithm for dual-rate ARX models

被引：19

作者：

Chen, Jing ^{[1
]}

Liu, Yanjun ^{[2
]}

Xu, Ling ^{[2
]}

机构：

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

[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2018年 / 32卷 / 11期

基金：

中国国家自然科学基金;

关键词：

auxiliary model; dual-rate system; new filter; parameter estimation; stochastic gradient; PARAMETER-ESTIMATION ALGORITHM; AUXILIARY-MODEL; NONLINEAR-SYSTEMS; IDENTIFICATION; STRATEGY; DESIGN; DELAY; STATE;

D O I：

10.1002/acs.2930

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 paper proposes a new filter-based stochastic gradient algorithm for dual-rate ARX models. Algorithm analysis is based upon the Kalman filter and smoother method. The new filter applies the measurable outputs to adjust the estimated outputs during each interval of the slow sampled rate. A comparative study reveals that the present consideration makes the estimated outputs more accurate than the classical auxiliary model. A stochastic gradient algorithm is developed for the estimation of parameters using all data. The simulation made further guarantees the usefulness of the proposed algorithm.

引用

页码：1557 / 1568

页数：12

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