Maximum Likelihood Multi-innovation Stochastic Gradient Estimation for Multivariate Equation-error Systems

This paper focuses on the parameter estimation problems of multivariate equation-error systems. A multi-innovation generalized extended stochastic gradient algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equa...

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Bibliographic Details
Published in:International journal of control, automation, and systems Vol. 16; no. 5; pp. 2528 - 2537
Main Authors: Liu, Lijuan, Ding, Feng, Wang, Cheng, Alsaedi, Ahmed, Hayat, Tasawar
Format: Journal Article
Language:English
Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.10.2018
Springer Nature B.V
제어·로봇·시스템학회
Subjects:
Algorithms
Antiship missiles
Control
Engineering
Filtration
Guidance systems
Kalman filters
Measurement techniques
Mechatronics
Missile control
Parameter estimation
Regular Papers
Robotics
Satellites
Sensors
Time lag
제어계측공학
multi-innovation
stochastic gradient
multivariate system
Maximum likelihood
ISSN:1598-6446, 2005-4092
Online Access:Get full text
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Summary:This paper focuses on the parameter estimation problems of multivariate equation-error systems. A multi-innovation generalized extended stochastic gradient algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification subsystems, each of which has only a parameter vector, and a coupled subsystem maximum likelihood multi-innovation stochastic gradient identification algorithm is developed for estimating the parameter vectors of these subsystems. The simulation results show that the coupled subsystem maximum likelihood multi-innovation stochastic gradient algorithm can generate more accurate parameter estimates and has faster convergence rates compared with the multi-innovation generalized extended stochastic gradient algorithm.
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http://link.springer.com/article/10.1007/s12555-017-0538-8
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-017-0538-8