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    Wyświetlanie 1-7 z 7
    Tytuł:
    Prediction of the compressive strength of environmentally friendly concrete using artificial neural network
    Autorzy:
    Kulisz, Monika
    Kujawska, Justyna
    Aubakirova, Zulfiya
    Zhairbaeva, Gulnaz
    Warowny, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/38432692.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    ANN
    compressive strength
    RCA
    MLP
    RBF
    Opis:
    The paper evaluated the possibility of using artificial neural network models for predicting the compressive strength (Fc) of concretes with the addition of recycled concrete aggregate (RCA). The artificial neural network (ANN) approaches were used for three variable processes modeling (cement content in the range of 250 to 400 kg/m3, percentage of recycled concrete aggregate from 25% to 100% and the ratios of water contents 0.45 to 0.6). The results indicate that the compressive strength of recycled concrete at 3, 7 and 28 days is strongly influenced by the cement content, %RCA and the ratios of water contents. It is found that the compressive strength at 3, 7 and 28 days decreases when increasing RCA from 25% to 100%. The obtained MLP and RBF networks are characterized by satisfactory capacity for prediction of the compressive strength of concretes with recycled concrete aggregate (RCA) addition. The results in statistical terms; correlation coefficient (R) reveals that the both ANN approaches are powerful tools for the prediction of the compressive strength.
    Źródło:
    Applied Computer Science; 2022, 18, 4; 68-81
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamically positioned ship steering making use of backstepping method and artificial neural networks
    Autorzy:
    Witkowska, A.
    Rynkiewicz, T. N.
    Powiązania:
    https://bibliotekanauki.pl/articles/260171.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    backstepping
    neural networks
    RBF
    dynamic ship positioning
    Opis:
    The article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type artificial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. The artificial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties. The proposed control system does not require precise knowledge of the model of ship dynamics and external disturbances, it also eliminates the problem of analytical determination of the regression matrix when designing the control law with the aid of the adaptive backstepping procedure.
    Źródło:
    Polish Maritime Research; 2018, 4; 5-12
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads
    Autorzy:
    Mansourkhaki, A.
    Berangi, M.
    Haghiri, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/124655.pdf
    Data publikacji:
    2018
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    MLP
    RBF
    neural network
    noise prediction
    road traffic noise
    Opis:
    Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effects on humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to find new approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LAeq) by measuring the traffic volume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficient of determination (R2) and the Mean Squared Error (MSE). Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.
    Źródło:
    Journal of Ecological Engineering; 2018, 19, 1; 113-121
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Efficient ANN Interference Cancelation for High Order Modulation over Rayleigh Fading Channel
    Autorzy:
    Bouguerra, F.
    Saidi, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/308382.pdf
    Data publikacji:
    2018
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    adaptive filter
    channel equalization
    M-QAM
    MLP
    RBF
    symbol decision making
    Opis:
    High order modulation (HOM) presents a key challenge in increasing spectrum efficiency in 4G and upcoming 5G communication systems. In this paper, two non-linear adaptive equalizer techniques based on multilayer perceptron (MLP) and radial basis function (RBF) are designed and applied on HOM to optimize its performance despite its high sensitivity to noise and channel distortions. The artificial neural network’s (ANN) adaptive equalizer architectures and learning methods are simplified to avoid more complexity and to ensure greater speed in symbol decision making. They will be compared with the following popular adaptive filters: least mean square (LMS) and recursive least squares (RLS), in terms of bit error rate (BER) and minimum square error (MSE) with 16, 64, 128, 256, 512 and 1024 quadrature amplitude modulation (QAM). By that, this work will show the advantage that the MLP equalizer has, in most cases, over RBF and traditional linear equalizers.
    Źródło:
    Journal of Telecommunications and Information Technology; 2018, 4; 75-80
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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:
    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:
    Wykorzystanie sztucznych sieci neuronowych typu RBF do predykcji plonu wybranych roślin zbożowych
    The use of artificial neuronal networks of the RBF type for prediction of yield of chosen cereal plants
    Autorzy:
    Boniecki, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/335789.pdf
    Data publikacji:
    2005
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
    Tematy:
    sztuczna sieć neuronowa
    RBF
    predykcja
    plon
    zboże
    symulacja komputerowa
    artificial neural network
    prediction
    yield
    cereal plant
    computer simulation
    Opis:
    Pojawiające się ostatnio metody, mające cechy sztucznej inteligencji, pozwalają na budowę modeli symulacyjnych, które realizują postawione zadania w oparciu o wzorce zaczerpnięte bezpośrednio z obserwacji przyrody [1]. Szczególną grupę stanowią techniki przetwarzania oparte na sztucznych sieciach neuronowych, będące w istocie komputerowymi symulatorami pracy mózgu [3]. Za pomocą modeli neuronowych można m.in. dokonać predykcji wielkości plonów płodów rolnych w oparciu o posiadane empiryczne dane, dotyczące zbiorów w latach ubiegłych. W pracy proponuje się wykorzystanie technik predykcyjnych, jakie m.in. reprezentują wybrane topologie sieci neuronowych, w szczególności sieci neuronowe typu RBF (Radial Basis Functions).
    Appearing recently methods, having guilds of artificial intelligence, permit on building of simulating models which realize assigned tasks on the basis of patterns taken directly with nature observation [1]. The processing techniques based on artificial neural networks create a special group, being in fact a computer simulators of brain work [3]. With the help of neuronal models it is possible to predict the expected crops yield on the basis of empirical data regarding crop yields in last summers. This work proposes utilization of prediction methods, which represent chosen topologies of neuronal nets among others, the RBF (Radial Basis Functions) neural network peculiarly.
    Źródło:
    Journal of Research and Applications in Agricultural Engineering; 2005, 50, 2; 15-19
    1642-686X
    2719-423X
    Pojawia się w:
    Journal of Research and Applications in Agricultural Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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