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Physical Chemistry Chemical Physics, 2022
Composition, size, and structure dependences of stable configuration of Pd–Ru alloy nanoparticles under finite temperature were theoretically investigated by using density functional theory calculation, multiple regression, and Wang–Landau sampling.
Yusuke Nanba, Michihisa Koyama
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Composition, size, and structure dependences of stable configuration of Pd–Ru alloy nanoparticles under finite temperature were theoretically investigated by using density functional theory calculation, multiple regression, and Wang–Landau sampling.
Yusuke Nanba, Michihisa Koyama
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Quantum Journal of Social Sciences and Humanities, 2022
The education model in the 21st century shall be learner-centered. Learners are expected to be independent to engage in self-directed learning with the integration of technological tools in developing necessary 21st century skills. However, the foundation of this education model shall not be neglected as positive emotion and motivation are the ...
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The education model in the 21st century shall be learner-centered. Learners are expected to be independent to engage in self-directed learning with the integration of technological tools in developing necessary 21st century skills. However, the foundation of this education model shall not be neglected as positive emotion and motivation are the ...
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Chemistry of Materials, 2017
We perform a large scale benchmark of machine learning methods for the prediction of the thermodynamic stability of solids. We start by constructing a data set that comprises density functional theory calculations of around 250000 cubic perovskite systems.
Jonathan Schmidt +5 more
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We perform a large scale benchmark of machine learning methods for the prediction of the thermodynamic stability of solids. We start by constructing a data set that comprises density functional theory calculations of around 250000 cubic perovskite systems.
Jonathan Schmidt +5 more
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2017 10th International Workshop on Multidimensional (nD) Systems (nDS), 2017
Iterative learning control can be applied to systems that execute the same finite duration task over and over again. This method control has been applied to many engineering systems, such as gantry robots and electrical motors. This paper gives further results on the design of dynamic iterative learning control laws using the repetitive process setting
Lukasz Hladowski +2 more
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Iterative learning control can be applied to systems that execute the same finite duration task over and over again. This method control has been applied to many engineering systems, such as gantry robots and electrical motors. This paper gives further results on the design of dynamic iterative learning control laws using the repetitive process setting
Lukasz Hladowski +2 more
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2016 IEEE 55th Conference on Decision and Control (CDC), 2016
Iterative learning control laws can be applied to systems that execute the same finite duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified.
Pavel Pakshin +4 more
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Iterative learning control laws can be applied to systems that execute the same finite duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified.
Pavel Pakshin +4 more
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2011 IEEE International Symposium on Intelligent Control, 2011
This paper considers iterative learning control for the practically relevant case of deterministic discrete linear plants where the first Markov parameter is zero. A 2D systems approach that uses a strong form of stability for linear repetitive processes is used to develop a one step control law design for both trial-to-trial error convergence and ...
Lukasz Hladowski +5 more
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This paper considers iterative learning control for the practically relevant case of deterministic discrete linear plants where the first Markov parameter is zero. A 2D systems approach that uses a strong form of stability for linear repetitive processes is used to develop a one step control law design for both trial-to-trial error convergence and ...
Lukasz Hladowski +5 more
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Analysis, 2022
Abstract Numerous computational and learning theory models have been studied using probabilistic functional equations. Especially in two-choice scenarios, the vast bulk of animal behavior research divides such situations into two different events. They split these actions into two possibilities according to the animals’ progress toward a
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Abstract Numerous computational and learning theory models have been studied using probabilistic functional equations. Especially in two-choice scenarios, the vast bulk of animal behavior research divides such situations into two different events. They split these actions into two possibilities according to the animals’ progress toward a
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Advances in Computational Mathematics, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mukherjee, Sayan +3 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mukherjee, Sayan +3 more
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Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2017
In this paper, a new learning algorithm based on Lyapunov stability theory for neural networks is used to improve the power system stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller.
Mehdi Arab Sadegh, Mohsen Farahani
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In this paper, a new learning algorithm based on Lyapunov stability theory for neural networks is used to improve the power system stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller.
Mehdi Arab Sadegh, Mohsen Farahani
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Stability of spectral learning algorithms: theory, methodologies and applications
In Data Mining an important research problem is the identification and analysis of theoretical properties that characterize and explain the behavior of learning algorithms. Based on such theoretical tools the comparison and analysis of algorithms can be based on rigorous and sound criteria other than simply their empirical behavior. In this context, anopenaire +1 more source