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Wyszukujesz frazę "Bas, E." wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting
Autorzy:
Bas, E.
Powiązania:
https://bibliotekanauki.pl/articles/91575.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
artificial neural networks
multiplicative neuron model
differential evolution
algorithm
forecasting
sztuczne sieci neuronowe
algorytm
prognozowanie
Opis:
In recent years, artificial neural networks have been commonly used for time series forecasting by researchers from various fields. There are some types of artificial neural networks and feed forward artificial neural networks model is one of them. Although feed forward artificial neural networks gives successful forecasting results they have a basic problem. This problem is architecture selection problem. In order to eliminate this problem, Yadav et al. (2007) proposed multiplicative neuron model artificial neural network. In this study, differential evolution algorithm is proposed for the training of multiplicative neuron model for forecasting. The proposed method is applied to two well-known different real world time series data.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 1; 5-11
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An arma type pi-sigma artificial neural network for nonlinear time series forecasting
Autorzy:
Akdeniz, E.
Egrioglu, E.
Bas, E.
Yolcu, U.
Powiązania:
https://bibliotekanauki.pl/articles/91816.pdf
Data publikacji:
2018
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
high order artificial neural networks
pi-sigma neural network, forecasting
recurrent neural network
particle swarm optimization (PSO)
Opis:
Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 121-132
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Influence of Melt Quality on the Formation of Fe Intermetallic in A360 Alloy
Autorzy:
Bas, E. N.
Alper, S.
Tuncay, Tansel
Dispinar, Derya
Kirtay, Sebahattin
Powiązania:
https://bibliotekanauki.pl/articles/2174621.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
A360
intermetallic
melt quality
cooling rate
intermetalik
jakość stopu
szybkość chłodzenia
Opis:
The solubility of Fe in aluminium alloys is known to be a problem in the casting of aluminium alloys. Due to the formation of various intermetallic phases, the mechanical properties decrease. Therefore, it is important to determine the formation mechanisms of such intermetallic. In this work, A360 alloy was used, and Fe additions were made. The alloy was cast into the sand and die moulds that consisted of three different thicknesses. In this way, the effect of the cooling rate was investigated. The holding time was selected to be 5 hours and every hour, a sample was collected from the melt for microstructural analysis. Additionally, the melt quality change was also examined by means of using a reduced pressure test where the bifilm index was measured. It was found that the iron content was increased after 2 hours of holding and the melt quality was decreased. There was a correlation between the duration and bifilm index. The size of Al-Si-Mn-Fe phases was increased in parallel with the bifilm content regardless of the iron content.
Źródło:
Archives of Foundry Engineering; 2022, 22, 3; 53--59
1897-3310
2299-2944
Pojawia się w:
Archives of Foundry Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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