Stochastic gradient-based particle filtering method for ARX models with nonlinear communication output submodel

被引：1
|
作者：
Feng, Jianxia [1 ,2 ]
Lu, Donglei [1 ,2 ]
机构：
[1] Nanjing Audit Univ, Jinshen Coll, Nanjing, Jiangsu, Peoples R China
[2] Wuxi Profess Coll Sci & Technol, Wuxi, Jiangsu, Peoples R China
来源：
INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL | 2019年 / 31卷 / 04期
关键词：
system identification; stochastic gradient; particle filter; missing outputs; auto regressivee xogenous; ARX model; IDENTIFICATION METHODS; SQUARES ALGORITHM; SYSTEMS; STATE;
D O I：
10.1504/IJMIC.2019.099823
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 develops a stochastic gradient-based modified particle filter algorithm for an auto regressivee xogenous (ARX) model with nonlinear communication output submodel. The outputs of the ARX model are transmitted over a nonlinear communication network, while the outputs of the communication network are available. Based on the modified particle filter and the available outputs, the outputs of the ARX model can be computed, and then the unknown parameters can be estimated by the stochastic gradient algorithm. The simulation results demonstrate that the stochastic gradient-based particle filter algorithm is effective.
引用
收藏
页码：331 / 336
页数：6
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