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Wyszukujesz frazę "multilayer perceptron" wg kryterium: Temat

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    Wyświetlanie 1-4 z 4
    Tytuł:
    Forecasting the Energy Consumption of an Industrial Enterprise Based on the Neural Network Model
    Autorzy:
    Kalinchyk, Vasyl
    Meita, Olexandr
    Pobigaylo, Vitalii
    Kalinchyk, Vitalii
    Filyanin, Danylo
    Powiązania:
    https://bibliotekanauki.pl/articles/2069887.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Koszalińska. Wydawnictwo Uczelniane
    Tematy:
    electrical load
    daily schedule
    modelling
    neural network
    multilayer perceptron
    MLP
    Opis:
    This research paper investigates the application of neural network models for forecasting in energy. The results of forecasting the weekly energy consumption of the enterprise according to the model of a multilayer perceptron at different values of neurons and training algorithms are given. The estimation and comparative analysis of models depending on model parameters is made.
    Źródło:
    Rocznik Ochrona Środowiska; 2021, 23; 484--492
    1506-218X
    Pojawia się w:
    Rocznik Ochrona Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The Investigation of the Applicability of Data-Driven Techniques in Hydrological Modeling: The Case of Seyhan Basin
    Autorzy:
    Turhan, Evren
    Keleş, Mümine Kaya
    Tantekin, Atakan
    Keleş, Abdullah Emre
    Powiązania:
    https://bibliotekanauki.pl/articles/1811777.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Koszalińska. Wydawnictwo Uczelniane
    Tematy:
    artificial neural networks
    drought analysis
    data mining
    Multilayer Perceptron
    Seyhan Basin
    Opis:
    Proper water resources planning and management is based on reliable hydrological data. Missing rainfall and runoff observation data, in particular, can cause serious risks in the planning of hydraulics structures. Hydrological modeling process is quitely complex. Therefore, using alternative estimation techniques to forecast missing data is reasonable. In this study, two data-driven techniques such as Artificial Neural Networks (ANN) and Data Mining were investigated in terms of availability in hydrology works. Feed Forward Back Propagation (FFBPNN) and Generalized Regression Neural Networks (GRNN) methods were performed on rainfall-runoff modeling for ANN. Besides, Hydrological drought analysis were examined using data mining technique. The Seyhan Basin was preferred to carry out these techniques. It is thought that the application of different techniques in the same basin could make a great contribute to the present work. Consequently, it is seen that FFBPNN is the best model for ANN in terms of giving the highest R2 and lowest MSE values. Multilayer Perceptron (MLP) algorithm was used to predict the drought type according to limit values. This system has been applied to show the relationship between hydrological data and measure the prediction accuracy of the drought analysis. According to the obtained data mining results, MLP algorithm gives the best accuracy results as flow observation stations using SRI-3 month data.
    Źródło:
    Rocznik Ochrona Środowiska; 2019, Tom 21, cz. 1; 29-51
    1506-218X
    Pojawia się w:
    Rocznik Ochrona Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex
    Autorzy:
    Kalinchyk, Vasyl
    Meita, Olexandr
    Pobigaylo, Vitalii
    Borychenko, Olena
    Kalinchyk, Vitalii
    Powiązania:
    https://bibliotekanauki.pl/articles/2174915.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Koszalińska. Wydawnictwo Uczelniane
    Tematy:
    classification
    modelling
    neural network
    radial basis function network
    RBF
    multilayer perceptron
    MLP
    Opis:
    This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognise the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for constructing neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters are made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification based on artificial neural networks is offered.
    Źródło:
    Rocznik Ochrona Środowiska; 2022, 24; 26--40
    1506-218X
    Pojawia się w:
    Rocznik Ochrona Środowiska
    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 1-4 z 4

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