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    Wyświetlanie 1-3 z 3
    Tytuł:
    Towards a very fast feedforward multilayer neural networks training algorithm
    Autorzy:
    Bilski, Jarosław
    Kowalczyk, Bartosz
    Kisiel-Dorohinicki, Marek
    Siwocha, Agnieszka
    Żurada, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2147135.pdf
    Data publikacji:
    2022
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    neural network training algorithm
    QR decomposition
    scaled Givens rotation
    approximation
    classification
    Opis:
    This paper presents a novel fast algorithm for feedforward neural networks training. It is based on the Recursive Least Squares (RLS) method commonly used for designing adaptive filters. Besides, it utilizes two techniques of linear algebra, namely the orthogonal transformation method, called the Givens Rotations (GR), and the QR decomposition, creating the GQR (symbolically we write GR + QR = GQR) procedure for solving the normal equations in the weight update process. In this paper, a novel approach to the GQR algorithm is presented. The main idea revolves around reducing the computational cost of a single rotation by eliminating the square root calculation and reducing the number of multiplications. The proposed modification is based on the scaled version of the Givens rotations, denoted as SGQR. This modification is expected to bring a significant training time reduction comparing to the classic GQR algorithm. The paper begins with the introduction and the classic Givens rotation description. Then, the scaled rotation and its usage in the QR decomposition is discussed. The main section of the article presents the neural network training algorithm which utilizes scaled Givens rotations and QR decomposition in the weight update process. Next, the experiment results of the proposed algorithm are presented and discussed. The experiment utilizes several benchmarks combined with neural networks of various topologies. It is shown that the proposed algorithm outperforms several other commonly used methods, including well known Adam optimizer.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 3; 181--195
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training
    Autorzy:
    Al_Duais, M. S.
    Mohamad, F. S.
    Powiązania:
    https://bibliotekanauki.pl/articles/307920.pdf
    Data publikacji:
    2017
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    artificial neural network (ANN)
    batch back propagation algorithm
    dynamic training rate
    speed up training
    accuracy training
    Opis:
    The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works.
    Źródło:
    Journal of Telecommunications and Information Technology; 2017, 4; 82-89
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel fast feedforward neural networks training algorithm
    Autorzy:
    Bilski, Jarosław
    Kowalczyk, Bartosz
    Marjański, Andrzej
    Gandor, Michał
    Zurada, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2031099.pdf
    Data publikacji:
    2021
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    neural network training algorithm
    QR decomposition
    Givens rotations
    approximation
    classification
    Opis:
    In this paper1 a new neural networks training algorithm is presented. The algorithm originates from the Recursive Least Squares (RLS) method commonly used in adaptive filtering. It uses the QR decomposition in conjunction with the Givens rotations for solving a normal equation - resulting from minimization of the loss function. An important parameter in neural networks is training time. Many commonly used algorithms require a big number of iterations in order to achieve a satisfactory outcome while other algorithms are effective only for small neural networks. The proposed solution is characterized by a very short convergence time compared to the well-known backpropagation method and its variants. The paper contains a complete mathematical derivation of the proposed algorithm. There are presented extensive simulation results using various benchmarks including function approximation, classification, encoder, and parity problems. Obtained results show the advantages of the featured algorithm which outperforms commonly used recent state-of-the-art neural networks training algorithms, including the Adam optimizer and the Nesterov’s accelerated gradient.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 4; 287-306
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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