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Wyszukujesz frazę "Khadir, M. T." wg kryterium: Autor

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    Wyświetlanie 1-3 z 3
    Tytuł:
    Comparison between GPC and adaptive GPC based on Takagi Sugeno multi-model for an Activated Sludge Reactor
    Autorzy:
    Matoug, L.
    Khadir, M. T.
    Powiązania:
    https://bibliotekanauki.pl/articles/206049.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    adaptive generalized predictive control
    generalized predictive control
    Takagi Sugeno
    activated sludge reactor
    activated sludge model
    Opis:
    This paper investigates the use of Adaptive Generalized Predictive Control (TS-AGPC) for an activated sludge reactor, based on a Takagi Sugeno (TS) model, and presents the comparison between the latter and Generalized Predictive Control using an overall TS model (TS-GPC). The reduced bio-reactor Activated Sludge ASM1 Model, which describes the biological degradation of an activated sludge reactor, is designed based on several simplifications, as a TS model, its structure being based on a set of linear submodels, covering the process input-output space, interpolated by a nonlinear weighting function µ. The adaptive GPC approach is obtained by switching between linear submodels of the TS formulation. This is performed by selecting, in turns, a portion of the weighting function µ. The winning model will then act as an internal model for the TS-AGPC control law formulation, whereas the complete TS model is used in the calculation of the TS-GPC control law. Finally, the performance under input and parametric disturbances as well as control variable constraints of the TS-AGPC controller are compared to those for a global TS-GPC controller and a benchmark PID in terms of error and response dynamics.
    Źródło:
    Control and Cybernetics; 2017, 46, 2; 147-176
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Introducing artificial neural network in ontologies alignment process
    Autorzy:
    Djeddi, W. E.
    Khadir, M. T.
    Powiązania:
    https://bibliotekanauki.pl/articles/206314.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    artificial neural network
    training
    ontology alignment
    WordNet
    XMap++
    Opis:
    Ontology alignment uses different similaritymeasures of different categories such as string, linguistic, and structural based similarity measures to understand ontologies’ semantics. A weights vector must, therefore, be assigned to these similarity measures, if a more accurate and meaningful alignment result is favored. Combining multiple measures into a single similarity metric has been traditionally solved using weights determined manually by an expert, Or calculated through general methods (e.g. average or sigmoid function) that do not provide optimal results. In this paper, we propose an artificial neural network algorithm to ascertain how to Combie multiple similarity measures into a single aggregated metric with the final aim of improving the ontology alignment quality. XMap++ is applied to benchmark tests at OAEI campaign 2010. Results show that neural network boosts the performance in most cases, and that the proposed novel approach is competitive with top-ranked system.
    Źródło:
    Control and Cybernetics; 2012, 41, 4; 743-759
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extension of first order predictive functional controllers to handle higher order internal models
    Autorzy:
    Khadir, M. T.
    Ringwood, J. V.
    Powiązania:
    https://bibliotekanauki.pl/articles/907939.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sterowanie predykcyjne
    system fazowy
    system oscylacyjny
    model predictive control
    predictive functional control
    non-minimum-phase systems
    oscillatory systems
    Opis:
    Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, there exists a range of process types which may present difficulties, leading to chattering and/or instability. In this paper, instability of first order PFC is addressed, and solutions to handle higher order and difficult systems are proposed. The input/output PFC formulation is extended to cover the cases of internal models with zero and/or higher order pole dynamics in an ARX/ARMAX form, via a parallel and cascaded model decomposition. Finally, a generic form of PFC, based on elementary outputs, is proposed to handle a wider range of higher order oscillatory and non-minimum phase systems. The range of solutions presented are supported by appropriate examples.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2008, 18, 2; 229-239
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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