Multi–Level Identification of Hammerstein-Wiener Systems
Autor: | Marcin Bieganski, Paweł Mielcarek, Grzegorz Mzyk |
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Rok vydání: | 2019 |
Předmět: |
0209 industrial biotechnology
Computer science 020208 electrical & electronic engineering Nonparametric statistics System identification 02 engineering and technology Least squares Nonlinear system Identification (information) 020901 industrial engineering & automation Control and Systems Engineering Kernel (statistics) Excited state 0202 electrical engineering electronic engineering information engineering Algorithm Parametric statistics |
Zdroj: | IFAC-PapersOnLine. 52:174-179 |
ISSN: | 2405-8963 |
DOI: | 10.1016/j.ifacol.2019.12.640 |
Popis: | The paper addresses the problem of Hammerstein–Wiener (N–L–N) system identification. The system is identified in so-called two-experiment approach. In passive experiment the system is excited with random noise, whereas in active experiment binary sequences are used. We present an algorithm with four consecutive stages, in which static nonlinear characteristics are recovered separately from the linear dynamic block. The proposed method uses both parametric and nonparametric identification tools. The estimates are based on kernel preselection of data and application of local least squares. Identification of output nonlinearity is processed under active experiment. We analyze the consistency of the proposed estimates under some a priori restrictions imposed on the excitation signal and system characteristics. Finally, we present a simple simulation example to demonstrate the behaviour of the algorithm. |
Databáze: | OpenAIRE |
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