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 "network structure" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-4 z 4
    Tytuł:
    Selected problem of structure optimization for Artificial Neural Networks with forward connections
    Autorzy:
    Płaczek, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/376117.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
    Tematy:
    artificial neural network
    network structure
    structure optimization
    Opis:
    The problem of Artificial Neural Network (ANN) structure optimization related to the definition of optimal number of hidden layers and distribution of neurons between layers depending on selected optimization criterion and inflicted constrains. The article presents the resolution of the optimization problem. The function describing the number of subspaces is given, and the minimum number of layers as well as the distribution of neurons between layers shall be found.
    Źródło:
    Poznan University of Technology Academic Journals. Electrical Engineering; 2014, 80; 191-197
    1897-0737
    Pojawia się w:
    Poznan University of Technology Academic Journals. Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A selected problem of the structure optimization and decomposition of the artificial neural network with cross-forward connections
    Autorzy:
    Płaczek, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/97313.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
    Tematy:
    artificial neural network
    structure optimization
    decomposition
    coordination
    cross connection
    Opis:
    The problem of an Artificial Neural Network (ANN) structure optimization is related to the definition of the optimal number of hidden layers and the distribution of neurons between layers depending on a selected optimization criterion and inflicted constrains. Using a hierarchical structure is an accepted default way of defining an ANN structure. The following article presents the resolution of the optimization problem. The function describing the number of subspaces is given, and the minimum number of layers, as well as the distribution of neurons between layers, shall be found. The structure can be described using different methods, mathematical tools, and software or/and technical implementation. The ANN decomposition into hidden and output layers - the first step to build a two-level learning algorithm for cross-forward connections structure - is described, too.
    Źródło:
    Computer Applications in Electrical Engineering; 2014, 12; 597-608
    1508-4248
    Pojawia się w:
    Computer Applications in Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural networks as a tool for georadar data processing
    Autorzy:
    Szymczyk, P.
    Tomecka-Suchoń, S.
    Szymczyk, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330009.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    neural network
    artificial neural network
    ground penetrating radar
    geological structure
    sinkhole
    sieć neuronowa
    sztuczna sieć neuronowa
    georadar
    penetracja gruntu
    budowa geologiczna
    zapadlisko górnicze
    Opis:
    In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2015, 25, 4; 955-960
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of CEC using fractal parameters by artificial neural networks
    Autorzy:
    Bayat, H.
    Davatgar, N.
    Jalali, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/25675.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Instytut Agrofizyki PAN
    Tematy:
    cation exchange capacity
    prediction
    fractal structure
    fractal theory
    particle size distribution
    artificial neural network
    pedotransfer function
    Opis:
    The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters.
    Źródło:
    International Agrophysics; 2014, 28, 2
    0236-8722
    Pojawia się w:
    International Agrophysics
    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