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    Wyświetlanie 1-3 z 3
    Tytuł:
    Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks
    Autorzy:
    Bilski, Jarosław
    Smoląg, Jacek
    Kowalczyk, Bartosz
    Grzanek, Konrad
    Izonin, Ivan
    Powiązania:
    https://bibliotekanauki.pl/articles/2201329.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    feed-forward neural network
    neural network learning algorithm
    Levenberg-Marquardt algorithm
    QR decomposition
    Givens rotation
    Opis:
    This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 45--61
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Local Levenberg-Marquardt algorithm for learning feedforwad neural networks
    Autorzy:
    Bilski, Jarosław
    Kowalczyk, Bartosz
    Marchlewska, Alina
    Zurada, Jacek M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1837415.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    feed-forward neural network
    neural network learning algorithm
    optimization problem
    Levenberg-Marquardt algorithm
    QR decomposition
    Givens rotation
    Opis:
    This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 299-316
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recurrent neural identification and control of a continuous bioprocess via first and second order learning
    Autorzy:
    Baruch, I.
    Mariaca-Gaspar, C. R.
    Powiązania:
    https://bibliotekanauki.pl/articles/385133.pdf
    Data publikacji:
    2010
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    backpropagation learning
    direct adaptive neural control
    indirect adaptive sliding mode control
    Kalman filter recurrent neural network identifier
    Levenberg-Marquardt learning
    Opis:
    This paper applies a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Mar quardt (L-M) learning algorithm capable to estimate para meters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural con trol schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 4; 37-52
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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