Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "group method of data handling" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-4 z 4
    Tytuł:
    The ERP class system objective assessment method
    Autorzy:
    Patalas, J.
    Krupa, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/117659.pdf
    Data publikacji:
    2005
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    ERP systems
    method GMDH
    group method of data handling
    Opis:
    This paper presents ERP class system objective assessment method when using the neural systems GMDH basing on the Ivachnienko algorithm. An approach to ERPs evaluation aimed at their successful implementation into a class of the small and medium-sized enterprises (SMEs) is considered. The set of performance indices supporting an ERP evaluation in the context of its implementation into a given SME is proposed. Consequently the decision model binding the selected indicators of effectiveness of SME implementation with the parameters of a given ERP system and the parameters of the company as such, which introduced this system is discussed.
    Źródło:
    Applied Computer Science; 2005, 1, 1; 115-132
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Shape Optimisation of Multi-Chamber Acoustical Plenums Using BEM, Neural Networks, and GA Method
    Autorzy:
    Chang, Y.-C.
    Cheng, H.-C.
    Chiu, M.-C.
    Chien, Y.-H.
    Powiązania:
    https://bibliotekanauki.pl/articles/177780.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    boundary element method
    plenum
    centre-opening baffle
    polynomial neural network model
    group method of data handling
    optimisation
    genetic algorithm
    Opis:
    Research on plenums partitioned with multiple baffles in the industrial field has been exhaustive. Most researchers have explored noise reduction effects based on the transfer matrix method and the boundary element method. However, maximum noise reduction of a plenum within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of multi-chamber plenums becomes essential. In this paper, two kinds of multi-chamber plenums (Case I: a two-chamber plenum that is partitioned with a centre-opening baffle; Case II: a three-chamber plenum that is partitioned with two centre-opening baffles) within a fixed space are assessed. In order to speed up the assessment of optimal plenums hybridized with multiple partitioned baffles, a simplified objective function (OBJ) is established by linking the boundary element model (BEM, developed using SYSNOISE) with a polynomial neural network fit with a series of real data – input design data (baffle dimensions) and output data approximated by BEM data in advance. To assess optimal plenums, a genetic algorithm (GA) is applied. The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.
    Źródło:
    Archives of Acoustics; 2016, 41, 1; 43-53
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method
    Autorzy:
    Chang, Y.-C.
    Chiu, M.-C.
    Xie, J.-L.
    Powiązania:
    https://bibliotekanauki.pl/articles/178126.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    finite element method
    polynomial neural network model
    genetic algorithm
    group method of data handling
    reciprocating compressor
    optimization
    Opis:
    Industry often utilizes acoustical hoods to block noise emitted from reciprocating compressors. However, the hoods are large and bulky. Therefore, to diminish the size of the compressor, a compact discharge muffler linked to the compressor outlet is considered. Because the geometry of a reciprocating compressor is irregular, COMSOL, a finite element analysis software, is adopted. In order to explore the acoustical performance, a mathematical model is established using a finite element method via the COMSOL commercialized package. Additionally, to facilitate the shape optimization of the muffler, a polynomial neural network model is adopted to serve as an objective function; also, a Genetic Algorithm (GA) is linked to the OBJ function. During the optimization, various noise abatement strategies such as a reverse expansion chamber at the outlet of the discharge muffler and an inner extended tube inside the discharge muffler, will be assessed by using the artificial neural network in conjunction with the GA optimizer. Consequently, the discharge muffler that is optimally shaped will decrease the noise of the reciprocating compressor.
    Źródło:
    Archives of Acoustics; 2017, 42, 2; 189-197
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    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 1-4 z 4

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies