NDLI: Maximum likelihood identification of dual-rate Hammerstein output-error moving average system

Content Provider	IET Digital Library
Author	Li, Junhong Zhang, Jiali
Abstract	This study discusses the parameter estimation of the Hammerstein output-error moving average system using the dual-rate sampled data. The polynomial transformation technique is used to obtain the identification model of the discussed dual-rate sampled systems. The stochastic gradient optimisation method is an effective optimisation method. Compared with the Newton optimisation, it only needs to calculate the first derivative during the optimisation and the amount of calculation is relatively small. It is a good choice to use the stochastic gradient algorithm for the identification of Hammerstein dual-rate model after using the polynomial transformation technique. In order to improve the convergence speed, a maximum likelihood forgetting factor stochastic gradient identification algorithm is proposed by combining the maximum likelihood principle and the gradient search method. The convergence of the algorithm is analysed by using the stochastic process theory. Furthermore, in order to improve the estimation accuracy of the identification algorithm, a maximum likelihood multi-innovation forgetting factor stochastic gradient algorithm is proposed by using the multi-innovation identification theory. The effectiveness of the proposed algorithms is illustrated by a numerical simulation example and a water tank system.
Starting Page	1089
Ending Page	1101
Page Count	13
ISSN	17518644
Volume Number	14
e-ISSN	17518652
Issue Number	Issue 8, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2019.0419
Journal	IET Control Theory & Applications
Publisher Date	2020-02-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Dual-rate Sampled Data Dual-rate Sampled System Gradient Method Gradient Search Method Graph Theory Hammerstein Dual-rate Model Hammerstein Output-error Moving Average System Identification Model Least Squares Approximation Maximum Likelihood Estimation Maximum Likelihood Identification Maximum Likelihood Multi-innovation Forgetting Factor Stochastic Gradient Identification Algorithm Maximum Likelihood Principle Modelling And Identification Multiple Innovation Identification Theory Newton Optimisation Optimisation Optimisation Technique Parameter Estimation Polynomial Polynomial Transformation Technique Probability Theory Set Theory Simulation Statistics Stochastic Gradient Algorithm Stochastic Gradient Optimisation Method Stochastic Linearised SCUC Stochastic Process Theory Water Tank System
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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