Maximum likelihood gradient-based iterative estimation for multivariable systems
- Author(s): Huafeng Xia 1 ; Yongqing Yang 1 ; Feng Ding 1, 2, 3 ; Ling Xu 1 ; Tasawar Hayat 3
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View affiliations
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Affiliations:
1:
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering , Jiangnan University , Wuxi 214122 , People's Republic of China ;
2: College of Automation and Electronic Engineering , Qingdao University of Science and Technology , Qingdao 266061 , People's Republic of China ;
3: Department of Mathematics , King Abdulaziz University , Jeddah 21589 , Saudi Arabia
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Affiliations:
1:
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering , Jiangnan University , Wuxi 214122 , People's Republic of China ;
- Source:
Volume 13, Issue 11,
23
July
2019,
p.
1683 – 1691
DOI: 10.1049/iet-cta.2018.6240 , Print ISSN 1751-8644, Online ISSN 1751-8652
This study concerns the parameter identification issues for a class of multivariable systems with moving average noise. The main contributions are to transform a multivariable system into several subsystems for reducing the computational complexity by means of the decomposition technique and to deal with the coloured noise for improving the estimation accuracy by using the maximum likelihood principle and the iterative estimation theory. As the maximum likelihood gradient-based iterative algorithm makes sufficient use of all the observed data at each iteration, the parameter estimation accuracy can be enhanced. The numerical simulation results demonstrate that the proposed algorithm has faster convergence rates and better tracking performance than the compared multivariable extended stochastic gradient algorithm.
Inspec keywords: convergence; stochastic processes; parameter estimation; gradient methods; multivariable systems; iterative methods; estimation theory; maximum likelihood estimation
Other keywords: iterative estimation theory; multivariable system; parameter estimation accuracy; average noise; iteration; compared multivariable extended stochastic gradient algorithm; parameter identification issues; maximum likelihood gradient-based; coloured noise; maximum likelihood principle
Subjects: Other topics in statistics; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Probability theory, stochastic processes, and statistics; Numerical approximation and analysis; Other topics in statistics
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