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    Wyświetlanie 1-6 z 6
    Tytuł:
    Smart control based on neural networks for multicellular converters
    Autorzy:
    Laidi, Kamel
    Bouchhida, Ouahid
    Nibouche, Mokhtar
    Benmansour, Khelifa
    Powiązania:
    https://bibliotekanauki.pl/articles/1841217.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multicellular converters
    neural networks
    smart control
    Opis:
    A smart control based on neural networks for multicellular converters has been developed and implemented. The approach is based on a behavioral description of the different converter operating modes. Each operating mode represents a well-defined configuration for which an operating zone satisfying given invariance conditions, depending on the capacitors’ voltages and the load current of the converter, is assigned. A control vector, whose components are the control signals to be applied to the converter switches is generated for each mode. Therefore, generating the control signals becomes a classification task of the different operating zones. For this purpose, a neural approach has been developed and implemented to control a 2-cell converter then extended to a 3-cell converter. The developed approach has been compared to super-twisting sliding mode algorithm. The obtained results demonstrate the approach effectiveness to provide an efficient and robust control of the load current and ensure the balancing of the capacitors voltages.
    Źródło:
    Archives of Electrical Engineering; 2021, 70, 3; 531-550
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Elman neural network for modeling and predictive control of delayed dynamic systems
    Autorzy:
    Wysocki, A.
    Ławryńczuk, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/229646.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    dynamic models
    process control
    model predictive control
    neural networks
    Elman neural network
    delayed systems
    Opis:
    The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
    Źródło:
    Archives of Control Sciences; 2016, 26, 1; 117-142
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Stator winding fault diagnosis of induction motor operating under the field-oriented control with convolutional neural networks
    Autorzy:
    Skowron, M.
    Wolkiewicz, M.
    Tarchała, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/200241.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    diagnostics
    stator faults
    field-oriented control
    convolutional neural networks
    Opis:
    In this paper deep neural networks are proposed to diagnose inter-turn short-circuits of induction motor stator windings operating under the Direct Field Oriented Control method. A convolutional neural network (CNN), trained with a Stochastic Gradient Descent with Momentum method is used. This kind of deep-trained neural network allows to significantly accelerate the diagnostic process compared to the traditional methods based on the Fast Fourier Transform as well as it does not require stationary operating conditions. To assess the effectiveness of the applied CNN-based detectors, the tests were carried out for variable load conditions and different values of the supply voltage frequency. Experimental results of the proposed induction motor fault detection system are presented and discussed.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1039-1048
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Sensorless DTC of induction motor using improved neural network switching state selector controller
    Autorzy:
    Messaif, I.
    Berkouk, E. M.
    Saadia, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/229864.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    direct torque control
    NPC three-level inverter
    switching table
    neural point potential
    neural networks
    induction motor
    Opis:
    The paper deals with development of sensorless Direct Torque Control (DTC) system based on neural network. This network is built to solve the task of proper switching states selection based on information about electromagnetic torque and stator flux (position and magnitude) of induction motor. In fact, this technique which uses conventional switching table is not convenient for one-line and real time control for its high computation time. In order to avoid this problem a solution based on neural network is proposed. Well trained Artificial Neural Network structure can replace successfully the switching table. However, in the Neutral-Point-Clamped topology, it has an inherent problem of Neutral Point Potential (NPP) variation. In this way, a Neural Network-Direct Torque Control technique has been applied and the estimated value of the Neutral Point Potential is used, which is calculated by motor currents. This control strategy offers the possibility of selecting appropriate switching state to achieve the control of Neutral Point Potential. Simulation results verify the validity of the proposed method.
    Źródło:
    Archives of Control Sciences; 2010, 20, 4; 435-456
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recurrent Neural Networks for Predictive Maintenance of Mill Fan Systems
    Autorzy:
    Koprinkova-Hristova, P. D.
    Hadjiski, M. B.
    Doukovska, L. A.
    Beloreshki, S. V.
    Powiązania:
    https://bibliotekanauki.pl/articles/226304.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    technical diagnosis
    Thermal Power Plant (TPP)
    Recurrent Neural Networks (RNN)
    distributed control system (DCS)
    predictive maintenance
    Opis:
    In the present paper we focus on online monitoring system for predictive maintenance based on sensor automated inputs. Our subject was a device from Maritsa East 2 power plant - a mill fan. The main sensor information we have access to is based on the vibration of the nearest to the mill rotor bearing block. Our aim was to create a (nonlinear) model able to predict on time possible changes in vibrations tendencies that can be early signal for system work deterioration. For that purpose, we compared two types of recurrent neural networks: historical Elman architecture and a recently developed kind of RNN named Echo stet networks (ESN). The preliminary investigations showed better approximation and faster training abilities of ESN in comparison to the Elman network. Direction of future work will be increasing of predications time horizon and inclusion of our predictor at lower level of a complex predictive maintenance system.
    Źródło:
    International Journal of Electronics and Telecommunications; 2011, 57, 3; 401-406
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Influence of IQT on research in ICT
    Autorzy:
    Bednarski, Bogdan J.
    Lepak, Łukasz E.
    Łyskawa, Jakub J.
    Pieńczuk, Paweł
    Rosoł, Maciej
    Romaniuk, Ryszard S.
    Powiązania:
    https://bibliotekanauki.pl/articles/2055259.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    ICT
    control theory
    IQT
    Information Quantum Technologies
    Quantum 2.0
    applications of IQT
    quantum systems
    qubit neural networks
    quantum time series forecasting;
    Quantum Reinforcement Learning
    Opis:
    This paper is written by a group of Ph.D. students pursuing their work in different areas of ICT, outside the direct area of Information Quantum Technologies IQT. An ambitious task was undertaken to research, by each co-author, a potential practical influence of the current IQT development on their current work. The research of co-authors span the following areas of ICT: CMOS for IQT, QEC, quantum time series forecasting, IQT in biomedicine. The intention of the authors is to show how quickly the quantum techniques can penetrate in the nearest future other, i.e. their own, areas of ICT.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 2; 259--266
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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