NDLI: Maximum likelihood gradient-based iterative estimation for multivariable systems

Content Provider	IET Digital Library
Author	Xia, Huafeng Yang, Yongqing Ding, Feng Xu, Ling Hayat, Tasawar
Abstract	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.
Starting Page	1683
Ending Page	1691
Page Count	9
ISSN	17518644
Volume Number	13
e-ISSN	17518652
Issue Number	Issue 11, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/13/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2018.6240
Journal	IET Control Theory & Applications
Publisher Date	2019-04-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Average Noise Coloured Noise Compared Multivariable Extended Stochastic Gradient Algorithm Convergence Estimation Theory Gradient Method Interpolation And Function Approximation Iteration Iterative Estimation Theory Iterative Method Maximum Likelihood Estimation Maximum Likelihood Gradient-based Maximum Likelihood Principle Multivariable System Numerical Analysis Numerical Approximation And Analysis Parameter Estimation Parameter Estimation Accuracy Parameter Identification Issues Probability Theory Statistics Stochastic Linearised SCUC
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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