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    Wyświetlanie 1-5 z 5
    Tytuł:
    Zastosowanie metody funkcji radialnych do analizy akustycznych drgań własnych
    Application radial basis function method for solve acoustical eigen value problem
    Autorzy:
    Majkut, L.
    Olszewski, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/251177.pdf
    Data publikacji:
    2013
    Wydawca:
    Instytut Naukowo-Wydawniczy TTS
    Tematy:
    metoda funkcji radialnych
    drgania własne
    kabina pojazdu
    radial basis function method
    eigenvalues
    vehicle cabin
    Opis:
    W artykule opisano możliwości zastosowania Metody Funkcji Radialnych do wyznaczania akustycznych częstotliwości drgań własnych w przestrzeniach ograniczonych. Porównano metodyki popularnych narzędzi obliczeniowych takich jak Metoda Elementów Skończonych i Metoda Elementów Brzegowych wraz ze wskazaniem wad i zalet do Metody Funkcji Radialnych.
    In the paper the possibility of Radial Basis Function Method for the calculation of acoustic eigenvalues is described. The proposed method is compared with other numerical methods of wave acoustic. The advantages and disadvantages of Finite Element Method and Boundary Element Method are described and compared to proposed Radial Basis Function Method.
    Źródło:
    TTS Technika Transportu Szynowego; 2013, 10; 1109-1115, CD
    1232-3829
    2543-5728
    Pojawia się w:
    TTS Technika Transportu Szynowego
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Meshless local radial point interpolation (MLRPI) for generalized telegraph and heat diffusion equation with non-local boundary conditions
    Autorzy:
    Shivanian, E.
    Khodayari, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/279501.pdf
    Data publikacji:
    2017
    Wydawca:
    Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
    Tematy:
    non-local boundary condition
    meshless local radial point interpolation (MLRPI) method
    local weak formulation
    radial basis function
    telegraph equation
    Opis:
    In this paper, the meshless local radial point interpolation (MLRPI) method is formulated to the generalized one-dimensional linear telegraph and heat diffusion equation with non-local boundary conditions. The MLRPI method is categorized under meshless methods in which any background integration cells are not required, so that all integrations are carried out locally over small quadrature domains of regular shapes, such as lines in one dimensions, circles or squares in two dimensions and spheres or cubes in three dimensions. A technique based on the radial point interpolation is adopted to construct shape functions, also called basis functions, using the radial basis functions. These shape functions have delta function property in the frame work of interpolation, therefore they convince us to impose boundary conditions directly. The time derivatives are approximated by the finite difference time- -stepping method. We also apply Simpson’s integration rule to treat the non-local boundary conditions. Convergency and stability of the MLRPI method are clarified by surveying some numerical experiments.
    Źródło:
    Journal of Theoretical and Applied Mechanics; 2017, 55, 2; 571-582
    1429-2955
    Pojawia się w:
    Journal of Theoretical and Applied Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of prototype selection algorithms used in construction of neural networks learned by SVD
    Autorzy:
    Jankowski, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/330020.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    radial basis function network
    extreme learning machine
    kernel method
    prototype selection
    machine learning
    k nearest neighbours
    radialna funkcja bazowa
    metoda jądrowa
    uczenie maszynowe
    metoda k najbliższych sąsiadów
    Opis:
    Radial basis function networks (RBFNs) or extreme learning machines (ELMs) can be seen as linear combinations of kernel functions (hidden neurons). Kernels can be constructed in random processes like in ELMs, or the positions of kernels can be initialized by a random subset of training vectors, or kernels can be constructed in a (sub-)learning process (sometimes by k-means, for example). We found that kernels constructed using prototype selection algorithms provide very accurate and stable solutions. What is more, prototype selection algorithms automatically choose not only the placement of prototypes, but also their number. Thanks to this advantage, it is no longer necessary to estimate the number of kernels with time-consuming multiple train-test procedures. The best results of learning can be obtained by pseudo-inverse learning with a singular value decomposition (SVD) algorithm. The article presents a comparison of several prototype selection algorithms co-working with singular value decomposition-based learning. The presented comparison clearly shows that the combination of prototype selection and SVD learning of a neural network is significantly better than a random selection of kernels for the RBFN or the ELM, the support vector machine or the kNN. Moreover, the presented learning scheme requires no parameters except for the width of the Gaussian kernel.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 719-733
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A fast neural network learning algorithm with approximate singular value decomposition
    Autorzy:
    Jankowski, Norbert
    Linowiecki, Rafał
    Powiązania:
    https://bibliotekanauki.pl/articles/330870.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Moore–Penrose pseudoinverse
    radial basis function network
    extreme learning machine
    kernel method
    machine learning
    singular value decomposition
    deep extreme learning
    principal component analysis
    pseudoodwrotność Moore–Penrose
    radialna funkcja bazowa
    maszyna uczenia ekstremalnego
    uczenie maszynowe
    analiza składników głównych
    Opis:
    The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks. The main idea of this algorithm lies in the usage of fast approximation of the Moore–Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O(mn2). We consider algorithms with a complexity of O(mnl), where l < n and l is often significantly smaller than n. Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 581-594
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Soft computing-based technique as a predictive tool to estimate blast-induced ground vibration
    Autorzy:
    Arthur, Clement Kweku
    Temeng, Victor Amoako
    Ziggah, Yao Yevenyo
    Powiązania:
    https://bibliotekanauki.pl/articles/1839011.pdf
    Data publikacji:
    2019
    Wydawca:
    Główny Instytut Górnictwa
    Tematy:
    radial basis function neural network
    back propagation neural network
    generalized regression neural network
    wavelet neural network
    group method of data handling
    ground vibration
    radialna funkcja bazowa
    sieć neuronowa
    GRNN
    sieć falkowo-neuronowa
    grupowa metoda przetwarzania danych
    drgania gruntu
    Opis:
    The safety of workers, the environment and the communities surrounding a mine are primary concerns for the mining industry. Therefore, implementing a blast-induced ground vibration monitoring system to monitor the vibrations emitted due to blasting operations is a logical approach that addresses these concerns. Empirical and soft computing models have been proposed to estimate blast-induced ground vibrations. This paper tests the efficiency of the Wavelet Neural Network (WNN). The motive is to ascertain whether the WNN can be used as an alternative to other widely used techniques. For the purpose of comparison, four empirical techniques (the Indian Standard, the United State Bureau of Mines, Ambrasey-Hendron, and Langefors and Kilhstrom) and four standard artificial neural networks of backpropagation (BPNN), radial basis (RBFNN), generalised regression (GRNN) and the group method of data handling (GMDH) were employed. According to the results obtained from the testing dataset, the WNN with a single hidden layer and three wavelons produced highly satisfactory and comparable results to the benchmark methods of BPNN and RBFNN. This was revealed in the statistical results where the tested WNN had minor deviations of approximately 0.0024 mm/s, 0.0035 mm/s, 0.0043 mm/s, 0.0099 and 0.0168 from the best performing model of BPNN when statistical indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Correlation Coefficient (R) and Coefficient of determination (R2) were considered.
    Źródło:
    Journal of Sustainable Mining; 2019, 18, 4; 287-296
    2300-1364
    2300-3960
    Pojawia się w:
    Journal of Sustainable Mining
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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