A
Adil Brouri
Researcher at Arts et Métiers ParisTech
Publications - 51
Citations - 481
Adil Brouri is an academic researcher from Arts et Métiers ParisTech. The author has contributed to research in topics: Nonlinear system & System identification. The author has an hindex of 9, co-authored 41 publications receiving 330 citations. Previous affiliations of Adil Brouri include University of Caen Lower Normandy & École Normale Supérieure.
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Brief paper: Identification of Hammerstein systems in presence of hysteresis-backlash and hysteresis-relay nonlinearities
TL;DR: The problem of identifying Hammerstein systems (HamSys) is addressed in presence of hysteresis-backlash (HB) and hystereis-relay (HR) nonlinearities and appropriate system parameterizations and least-squares like parameter estimators are designed.
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Frequency identification of nonparametric Wiener systems containing backlash nonlinearities
TL;DR: This paper addresses the problem of identifying Wiener systems constituted of nonparametric linear dynamics and backlash nonlinearities and develops a frequency identification method to estimate the system frequency response function and the backlash non linearity borders.
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Frequency Identification of Hammerstein-Wiener Systems with Backlash Input Nonlinearity
TL;DR: The problem of system identification is addressed for Hammerstein-Wiener systems that involve memory operator of backlash type bordered by straight lines as input nonlinearity by using easily generated excitation signals.
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Combined frequency-prediction error identification approach for Wiener systems with backlash and backlash-inverse operators
TL;DR: Wiener systems identification is studied in the presence of possibly infinite-order linear dynamics and memory nonlinear operators of backlash and backlash-inverse types, finding that the borders are allowed to be noninvertible and crossing making possible to account for memory and memoryless nonlinearities.
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Identification of hammerstein-wiener systems with backlash input nonlinearity bordered by straight lines
TL;DR: In this article, an optimal strategy is presented to identify the system nonlinearities and an identification approach is developed that provides estimates of the linear subsystem, which involves easily generated excitation signals.