Open AccessMaximum likelihood identification of dual‐rate Hammerstein output‐error moving average system
Author(s) -
Li Junhong,
Zhang Jiali
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.0419
Subject(s) - rate of convergence , convergence (economics) , transformation (genetics) , polynomial , dual (grammatical number) , estimation theory , mathematical optimization , mathematics , identification (biology) , gradient method , control theory (sociology) , system identification , parameter identification problem , algorithm , computer science , data modeling , model parameter , artificial intelligence , art , channel (broadcasting) , database , economic growth , computer network , mathematical analysis , chemistry , literature , biology , biochemistry , control (management) , botany , economics , gene
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.