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Identification for Wiener‐Hammerstein systems under quantized inputs and quantized output observations
Author(s) -
Guo Jin,
Zhao Yanlong
Publication year - 2021
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.2237
Subject(s) - identifiability , convergence (economics) , mathematics , nonlinear system , identification (biology) , system identification , rate of convergence , measure (data warehouse) , transformation (genetics) , function (biology) , control theory (sociology) , convex function , regular polygon , mathematical optimization , computer science , statistics , geometry , channel (broadcasting) , database , artificial intelligence , economic growth , computer network , chemistry , biology , biochemistry , control (management) , quantum mechanics , evolutionary biology , physics , botany , economics , gene
Abstract This paper investigates the Wiener‐Hammerstein system identification with quantized inputs and quantized output observations. By parameterizing the static nonlinear function, system identifiability is discussed first. Then, for the identifiable system a three‐step algorithm is proposed to estimate the unknown parameters by employing the empirical measure‐based method and the quasi‐convex combination technique. Finally, the algorithm is proved to be strongly convergent, the mean‐square convergence rate is presented, and the asymptotic efficiency is given by selecting a suitable transformation matrix. A numerical simulation is included to demonstrate the main results obtained.