Maximum likelihood gradient-based iterative estimation algorithm for a class of input nonlinear controlled autoregressive ARMA systems

被引：14
|
作者：
Chen, Feiyan [1 ]
Ding, Feng [1 ]
Li, Junhong [2 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Nantong Univ, Sch Elect Engn, Nantong 226019, Peoples R China
来源：
NONLINEAR DYNAMICS | 2015年 / 79卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Maximum likelihood; Stochastic gradient; Simulation; H-INFINITY CONTROL; PARAMETER-ESTIMATION; STOCHASTIC-SYSTEMS; IDENTIFICATION METHOD; HAMMERSTEIN SYSTEMS; STATE; CONJUGATE;
D O I：
10.1007/s11071-014-1712-7
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
This paper considers the parameter estimation problem for an input nonlinear controlled autoregressive ARMA model. The basic idea is to combine the maximum likelihood principle and the gradient search and to present a maximum likelihood gradient-based iterative estimation algorithm. The analysis and simulation results show that the proposed algorithm can effectively estimate the parameters of the input nonlinear controlled autoregressive ARMA systems.
引用
收藏
页码：927 / 936
页数：10
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