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    Wyświetlanie 1-3 z 3
    Tytuł:
    Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method
    Autorzy:
    Pajchrowski, T.
    Siwek, P.
    Wójcik, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/201068.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Reinforcement Learning
    adaptive control
    electric drive
    machine learning
    Opis:
    The paper presents a method for designing a neural speed controller with use of Reinforcement Learning method. The controlled object is an electric drive with a synchronous motor with permanent magnets, having a complex mechanical structure and changeable parameters. Several research cases of the control system with a neural controller are presented, focusing on the change of object parameters. Also, the influence of the system critic behaviour is researched, where the critic is a function of control error and energy cost. It ensures long term performance stability without the need of switching off the adaptation algorithm. Numerous simulation tests were carried out and confirmed on a real stand.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1019-1030
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Discrete uncertainty quantification for offline reinforcement learning
    Autorzy:
    Pérez Torres, Jose Luis
    Corrochano Jiménez, Javier
    García, Javier
    Majadas, Rubén
    Ibañez-Llano, Cristina
    Pérez, Sergio
    Fernández, Fernando
    Powiązania:
    https://bibliotekanauki.pl/articles/23944835.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    off-line reinforcement learning
    uncertainty quantification
    machine learning
    Opis:
    In many Reinforcement Learning (RL) tasks, the classical online interaction of the learning agent with the environment is impractical, either because such interaction is expensive or dangerous. In these cases, previous gathered data can be used, arising what is typically called Offline RL. However, this type of learning faces a large number of challenges, mostly derived from the fact that exploration/exploitation trade-off is overshadowed. In addition, the historical data is usually biased by the way it was obtained, typically, a sub-optimal controller, producing a distributional shift from historical data and the one required to learn the optimal policy. In this paper, we present a novel approach to deal with the uncertainty risen by the absence or sparse presence of some state-action pairs in the learning data. Our approach is based on shaping the reward perceived from the environment to ensure the task is solved. We present the approach and show that combining it with classic online RL methods make them perform as good as state of the art Offline RL algorithms such as CQL and BCQ. Finally, we show that using our method on top of established offline learning algorithms can improve them.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 4; 273--287
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks
    Autorzy:
    Kalnoor, Gauri
    Gowrishankar, -
    Powiązania:
    https://bibliotekanauki.pl/articles/1839336.pdf
    Data publikacji:
    2021
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    DDoS
    intrusion detection
    IoT
    machine learning
    Markov decision process
    MDP
    Q-learning
    NSL-KDD
    reinforcement learning
    Opis:
    In this paper, a new reinforcement learning intrusion detection system is developed for IoT networks incorporated with WSNs. A research is carried out and the proposed model RL-IDS plot is shown, where the detection rate is improved. The outcome shows a decrease in false alarm rates and is compared with the current methodologies. Computational analysis is performed, and then the results are compared with the current methodologies, i.e. distributed denial of service (DDoS) attack. The performance of the network is estimated based on security and other metrics.
    Źródło:
    Journal of Telecommunications and Information Technology; 2021, 3; 42-49
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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