Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering

被引：155

作者：

Ding, Feng ^{[1
,2
,3
]}

Wang, Feifei ^{[1
]}

Xu, Ling ^{[1
]}

Wu, Minghu ^{[2
]}

机构：

[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Sch Internet Things Engn, Wuxi 214122, Peoples R China

[2] Hubei Univ Technol, Hubei Collaborat Innovat Ctr High Efficiency Util, Wuhan 430068, Peoples R China

[3] Nanchang Hangkong Univ, Sch Informat Engn, Nanchang 330063, Jiangxi, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2017年 / 354卷 / 03期

基金：

中国国家自然科学基金;

关键词：

PARAMETER-ESTIMATION; HAMMERSTEIN SYSTEMS; NONLINEAR-SYSTEMS; AUXILIARY MODEL; DYNAMICAL-SYSTEMS; NEWTON ITERATION; CONVERGENCE; NOISE;

D O I：

10.1016/j.jfranklin.2016.11.030

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 decomposition based least squares iterative identification algorithm for multivariate pseudo-linear autoregressive moving average systems using the data filtering. The key is to apply the data filtering technique to transform the original system to a hierarchical identification model, and to decompose this model into three subsystems and to identify each subsystem, respectively. Compared with the least squares based iterative algorithm, the proposed algorithm requires less computational efforts. The simulation results show that the proposed algorithms can work well. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：1321 / 1339

页数：19

共 43 条

[1]

[Anonymous], 1999, SYSTEM IDENTIFICATIO

[2] Multi-AUV Target Search Based on Bioinspired Neurodynamics Model in 3-D Underwater Environments [J].

Cao, Xiang ;

Zhu, Daqi ;

Yang, Simon X. .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (11) :2364-2374

[3]

Chu Z.Z., IEEE T NEURAL NETW L

[4] Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition [J].

Ding, Feng ;

Liu, Ximei ;

Ma, Xingyun .

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2016, 301 :135-143

[5] The recursive least squares identification algorithm for a class of Wiener nonlinear systems [J].

Ding, Feng ;

Liu, Ximei ;

Liu, Manman .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (07) :1518-1526

[6] Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition [J].

Ding, Feng ;

Wang, Xuehai ;

Chen, Qijia ;

Xiao, Yongsong .

CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2016, 35 (09) :3323-3338

[7] An auxiliary model based least squares algorithm for a dual-rate state space system with time-delay using the data filtering [J].

Ding, Feng ;

Liu, Ximei ;

Gu, Ya .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (02) :398-408

[8] Decomposition Based Newton Iterative Identification Method for a Hammerstein Nonlinear FIR System with ARMA Noise [J].

Ding, Feng ;

Deng, Kepo ;

Liu, Ximei .

CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2014, 33 (09) :2881-2893

[9] Array Factor Forming for Image Reconstruction of One-Dimensional Nonuniform Aperture Synthesis Radiometers [J].

Feng, Li ;

Wu, Minghu ;

Li, Qingxia ;

Chen, Ke ;

Li, Yufang ;

He, Zhangqing ;

Tong, Jing ;

Tu, Lingying ;

Xie, Honggang ;

Lu, Hailiang .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (02) :237-241

[10]

Goodwin G. C., 1984, Adaptive filtering prediction and control

← 1 2 3 4 5 →