Open AccessHammerstein system identification with quantised inputs and quantised output observations
Author(s) -
Guo Jin,
Liu Haitao
Publication year - 2017
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.2016.1113
Subject(s) - identifiability , convergence (economics) , control theory (sociology) , rate of convergence , identification (biology) , system identification , function (biology) , mathematics , transformation (genetics) , estimation theory , upper and lower bounds , cramér–rao bound , matrix (chemical analysis) , computer science , algorithm , data modeling , statistics , mathematical analysis , control (management) , database , artificial intelligence , biology , botany , channel (broadcasting) , materials science , economic growth , computer network , chemistry , composite material , biochemistry , evolutionary biology , economics , gene
This study focuses on a discrete‐time Hammerstein system to investigate the identification with quantised inputs and quantised output observations. After the discussion of the system identifiability and by parameterising the static non‐linear function, a three‐step algorithm is proposed to estimate the unknown parameters for the identifiable system. The strong convergence and the mean‐square convergence rate of the algorithm are established. It is shown that the asymptotic efficiency can be achieved in terms of the Cramér–Rao lower bound by selecting a suitable transformation matrix. A numerical simulation is given to demonstrate the effectiveness of the algorithm.