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Wyszukujesz frazę "Liu, M. S." wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
Elimination of dominated strategies and inessential players
Autorzy:
Kaneko, M.
Liu, S.
Powiązania:
https://bibliotekanauki.pl/articles/406320.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
dominated strategies
inessential players
iterated elimination
order-independence
estimation of initial games
Opis:
We study the process, called the IEDI process, of iterated elimination of (strictly) dominated strategies and inessential players for finite strategic games. Such elimination may reduce the size of a game considerably, for example, from a game with a large number of players to one with a few players. We extend two existing results to our context; the preservation of Nash equilibria and orderindependence. These give a way of computing the set of Nash equilibria for an initial situation from the endgame. Then, we reverse our perspective to ask the question of what initial situations end up at a given final game. We assess what situations underlie an endgame. We give conditions for the pattern of player sets required for a resulting sequence of the IEDI process to an endgame. We illustrate our development with a few extensions of the battle of the sexes.
Źródło:
Operations Research and Decisions; 2015, 25, 1; 33-54
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effect of Strain Rate on the Microstructure of Warm-Deformed Ultrafined Medium-Carbon Steel
Autorzy:
Yuan, Q.
Xu, G.
Liu, S.
Liu, M.
Hu, H.
Powiązania:
https://bibliotekanauki.pl/articles/355659.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
warm deformation
medium-carbon steel
ultrafine grain
strain rate
Fe3C
Opis:
In this study, medium-carbon steel was subjected to warm deformation experiments on a Gleeble 3500 thermosimulator machine at temperatures of 550°C and 650°C and strain rates of 0.001 s-1 to 1 s-1. The warm deformation behavior of martensite and the effects of strain rate on the microstructure of ultrafine grained medium-carbon steel were investigated. The precipitation behavior of Fe3C during deformation was analyzed and the results showed that recrystallization occurred at a low strain rate. The average ultrafine ferrite grains of 500 ± 58 nm were fabricated at 550°C and a strain rate of 0.001 s-1. In addition, the size of Fe3C particles in the ferrite grains did not show any apparent change, while that of the Fe3C particles at the grain boundaries was mainly affected by the deformation temperature. The size of Fe3C particles increased with the increasing deformation temperature, while the strain rate had no significant effect on Fe3C particles. Moreover, the grain size of recrystallized ferrite decreased with an increase in the strain rate. The effects of the strain rate on the grain size of recrystallized ferrite depended on the deformation temperature and the strain rate had a prominent effect on the grain size at 550°C deformation temperature. Finally, the deformation resistance apparently decreased at 550°C and strain rate of 1 s-1 due to the maximum adiabatic heating in the material.
Źródło:
Archives of Metallurgy and Materials; 2018, 63, 4; 1805-1813
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Convergence Analysis of Multilayer Feedforward Networks Trained with Penalty Terms: A review
Autorzy:
Wang, J.
Yang, G.
Liu, S.
Zurada, J. M.
Powiązania:
https://bibliotekanauki.pl/articles/108639.pdf
Data publikacji:
2015
Wydawca:
Społeczna Akademia Nauk w Łodzi
Tematy:
Gradient
feedforward neural networks
generalization
penalty
convergence
pruning algorithms
Opis:
Gradient descent method is one of the popular methods to train feedforward neural networks. Batch and incremental modes are the two most common methods to practically implement the gradient-based training for such networks. Furthermore, since generalization is an important property and quality criterion of a trained network, pruning algorithms with the addition of regularization terms have been widely used as an efficient way to achieve good generalization. In this paper, we review the convergence property and other performance aspects of recently researched training approaches based on different penalization terms. In addition, we show the smoothing approximation tricks when the penalty term is non-differentiable at origin.
Źródło:
Journal of Applied Computer Science Methods; 2015, 7 No. 2; 89-103
1689-9636
Pojawia się w:
Journal of Applied Computer Science Methods
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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