Multi-innovation Stochastic Gradient Parameter Estimation for Input Nonlinear Controlled Autoregressive Models

被引：26

作者：

Xiao, Yongsong ^{[4
]}

Song, Guanglei ^{[3
]}

Liao, Yuwu ^{[2
]}

Ding, Ruifeng ^{[1
]}

机构：

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

[2] Xiangfan Univ, Sch Phys & Elect Engn, Xiangfan 441053, Peoples R China

[3] Wuxi Inst Technol, Sch Mech Technol, Wuxi 214121, Peoples R China

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

来源：

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2012年 / 10卷 / 03期

关键词：

Hammerstein model; multi-innovation identification; parameter estimation; recursive identification; stochastic gradient; system modeling; DUAL-RATE SYSTEMS; IDENTIFICATION METHODS; AUXILIARY MODEL; PERFORMANCE ANALYSIS; ITERATIVE SOLUTIONS; OUTPUT ESTIMATION; MATRIX EQUATIONS; ARMAX SYSTEMS; ALGORITHMS;

D O I：

10.1007/s12555-012-0322-8

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 multi-innovation stochastic gradient (MISG) parameter estimation algorithm for an input nonlinear controlled autoregressive (IN-CAR) model, i.e., a Hammerstein nonlinear CAR system, by expanding the innovation length. The analysis and simulation results indicate that the proposed MISG algorithm can generate more accurate parameter estimates for IN-CAR systems compared with the stochastic gradient algorithm.

引用

页码：639 / 643

页数：5

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