NDLI: Parameter estimation of Markov-switching Hammerstein systems using the variational Bayesian approach

Content Provider	IET Digital Library
Author	Ma, Junxia Huang, Biao Ding, Feng
Abstract	Parameter estimation of Markov-switching Hammerstein systems is presented in this study. The random switching mode is described by a hidden Markov model. An input non-linear controlled autoregressive Hammerstein model is applied to describe the dynamic process of each local model. By applying the decomposition technique to the non-linear mapping of the input signal, each local model is written as a linear-in-parameter form. The variational Bayesian approach is applied to identify the switched Markov Hammerstein models. Both the discrete-valued switching mode state and the unknown parameters in each local model are estimated. Moreover, instead of the point estimation, posterior distributions of unknown parameters for each local model are obtained. Numerical simulation examples and a hybrid tank experiment are presented to verify the effectiveness of the proposed approach.
Starting Page	1646
Ending Page	1655
Page Count	10
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.2018.6236
Journal	IET Control Theory & Applications
Publisher Date	2019-04-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Autoregressive Moving Average Processes Autoregressive Processes Bayes Method Hidden Markov Model Input Nonlinear Controlled Autoregressive Hammerstein Model Least Squares Approximation Linear-in-parameter Form Local Model Markov Processes Markov-switching Hammerstein System Nonlinear Control System Nonlinear Mapping Numerical Approximation And Analysis Parameter Estimation Point Estimation Probability Theory Random Switching Mode Statistics Stochastic Linearised SCUC Switched Markov Hammerstein Model Unknown Parameter Variational Bayesian Approach
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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