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    Wyświetlanie 1-3 z 3
    Tytuł:
    Prediction of industrial pollution by radial basis function networks
    Autorzy:
    Djebbri, N.
    Rouainia, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/207579.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    forecasting
    RBF
    artificial neural network
    pollution
    prognozowanie
    sztuczna sieć neuronowa
    zanieczyszczenie
    Opis:
    Atmospheric pollution has been receiving a significant interest for several decades since industries cause more and more pollution. Thanks to the development of many prediction techniques, scientists and industries are focusing more on pollution prediction. The aim of this work is to predict the two pollutant concentrations (NOx and CO) in industrial sites by a modified radial basis function (RBF) based neural network. The modification considered the spread parameter h of the activation function in the RBF network. In order to get the best network, the variations of this parameter for three cases were considered. In the first case, only pollutants concentrations variables were used, while in the second one, only the meteorological variables were utilized. In the third case, pollutants' concentrations were connected with meteorological variables. Based on calculation errors, the best model that ensures the best monitoring of pollutants concentration could be identified.
    Źródło:
    Environment Protection Engineering; 2018, 44, 3; 153-164
    0324-8828
    Pojawia się w:
    Environment Protection Engineering
    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
    Tytuł:
    Comparison of MLP and RBF Neural Networks in the Task of Classifying the Diameters of Water Pipes
    Autorzy:
    Gvishiani, Zurab
    Dawidowicz, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2174907.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Koszalińska. Wydawnictwo Uczelniane
    Tematy:
    water distribution system
    hydraulic calculation
    selection of diameter
    water pipe
    artificial neural network
    radial basis function
    multilayer perceptron
    Opis:
    Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.
    Źródło:
    Rocznik Ochrona Środowiska; 2022, 24; 505--519
    1506-218X
    Pojawia się w:
    Rocznik Ochrona Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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