Autor: Ben Hmida, F. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
Autorzy:: Soltani, M.
Chaari, A.
Ben Hmida, F.
Powiązania:: https://bibliotekanauki.pl/articles/330134.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: model rozmyty Takagi-Sugeno
algorytm grupowania
metoda najmniejszych kwadratów
optymalizacja rojem cząstek
Takagi-Sugeno fuzzy models
noise clustering algorithm
fuzzy c-regression model
orthogonal least squares
particle swarm optimization (PSO)
Opis:: This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 617-628
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Novel optimal recursive filter for state and fault estimation of linear stochastic systems with unknown disturbances
Autorzy:: Khémiri, K.
Ben Hmida, F.
Ragot, J.
Gossa, M.
Powiązania:: https://bibliotekanauki.pl/articles/930170.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: filtracja Kalmana
estymacja stanu
zakłócenie nieznane
system liniowy
system dyskretno-czasowy
Kalman filtering
minimum variance estimation
state estimation
fault estimation
unknown disturbances
linear discrete time systems
Opis:: This paper studies recursive optimal filtering as well as robust fault and state estimation for linear stochastic systems with unknown disturbances. It proposes a new recursive optimal filter structure with transformation of the original system. This transformation is based on the singular value decomposition of the direct feedthrough matrix distribution of the fault which is assumed to be of arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance criteria. Two numerical examples are given in order to illustrate the proposed method, in particular to solve the estimation of the simultaneous actuator and sensor fault problem and to make a comparison with the existing literature results.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2011, 21, 4; 629-637
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: An H∞ sliding mode observer for Takagi–Sugeno nonlinear systems with simultaneous actuator and sensor faults
Autorzy:: Ben Brahim, A.
Dhahri, S.
Ben Hmida, F.
Sellami, A.
Powiązania:: https://bibliotekanauki.pl/articles/330626.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: fault reconstruction
fault estimation
simultaneous fault
H∞ sliding mode observer
uncertain Takagi Sugeno system
LMI optimization
Opis:: This paper considers the problem of robust reconstruction of simultaneous actuator and sensor faults for a class of uncertain Takagi–Sugeno nonlinear systems with unmeasurable premise variables. The proposed fault reconstruction and estimation design method with H∞ performance is used to reconstruct both actuator and sensor faults when the latter are transformed into pseudo-actuator faults by introducing a simple filter. The main contribution is to develop a sliding mode observer (SMO) with two discontinuous terms to solve the problem of simultaneous faults. Sufficient stability conditions in terms linear matrix inequalities are achieved to guarantee the stability of the state estimation error. The observer gains are obtained by solving a convex multiobjective optimization problem. Simulation examples are given to illustrate the performance of the proposed observer.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2015, 25, 3; 547-559
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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