Filtering based multi-innovation extended stochastic gradient algorithm for Hammerstein nonlinear system modeling

This paper considers parameter estimation problems of Hammerstein finite impulse response moving average (FIR-MA) systems. In order to provide highly accurate parameter estimates and improve the convergence rate, a data filtering based multi-innovation extended stochastic gradient algorithm is prese...

Full description

Saved in:
Bibliographic Details
Published in:Applied mathematical modelling Vol. 39; no. 18; pp. 5724 - 5732
Main Authors: Wang, Ziyun, Wang, Yan, Ji, Zhicheng
Format: Journal Article
Language:English
Published: Elsevier Inc 15.09.2015
Subjects:
Filtering
Gradient search
Nonlinear system
Parameter estimation
System modeling
System modeling
Gradient search
Parameter estimation
Filtering
Nonlinear system
ISSN:0307-904X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper considers parameter estimation problems of Hammerstein finite impulse response moving average (FIR-MA) systems. In order to provide highly accurate parameter estimates and improve the convergence rate, a data filtering based multi-innovation extended stochastic gradient algorithm is presented to estimate the parameters of Hemmerstein FIR-MA systems by using the current innovation and past innovations. The simulation results show that the proposed algorithm can effectively estimate the parameters of the Hammerstein FIR-MA systems.
ISSN:0307-904X
DOI:10.1016/j.apm.2013.06.016