Results 251 to 260 of about 919,498 (296)

Predicting the stability of ternary intermetallics with density functional theory and machine learning

The Journal of Chemical Physics, 2018
We use a combination of machine learning techniques and high-throughput density-functional theory calculations to explore ternary compounds with the AB2C2 composition. We chose the two most common intermetallic prototypes for this composition, namely, the tI10-CeAl2Ga2 and the tP10-FeMo2B2 structures.
Jonathan Schmidt, Liming Chen, Silvana Botti, Miguel A. L. Marques   +3 more
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STABILITY RESULTS IN LEARNING THEORY

Analysis and Applications, 2005
The problem of proving generalization bounds for the performance of learning algorithms can be formulated as a problem of bounding the bias and variance of estimators of the expected error. We show how various stability assumptions can be employed for this purpose.
Alexander Rakhlin, Sayan Mukherjee, Tomaso Poggio   +2 more
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Analysis of robust control using stability theory of universal learning networks

IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028), 2003
Nth order asymptotic orbital stability analysis method has been proposed to determine whether a nonlinear system is stable or not with large fluctuations of the system states. In this paper, we discuss the stability of robust control of a nonlinear crane system using this method.
Yunqing Yu, Kotaro Hirasawa, Jinglu Hu, Junichi Murata   +3 more
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Principled reward shaping for reinforcement learning via lyapunov stability theory

Neurocomputing, 2020
Abstract Reinforcement learning (RL) suffers from the designation in reward function and the large computational iterating steps until convergence. How to accelerate the training process in RL plays a vital role. In this paper, we proposed a Lyapunov function based approach to shape the reward function which can effectively accelerate the training ...
Yunlong Dong, Xiuchuan Tang, Ye Yuan
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Thermodynamic stability of Pd–Ru alloy nanoparticles: combination of density functional theory calculations, supervised learning, and Wang–Landau sampling

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.
Yūsuke Nanba, Michihisa Koyama
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Experimentally verified Iterative Learning Control based on repetitive process stability theory

2012 American Control Conference (ACC), 2012
This paper gives new results on the design and experimental evaluation of an Iterative Learning Control (ILC) law in a repetitive process setting. The experimental results given are from a gantry robot facility that has been extensively used in the benchmarking of linear model based ILC designs.
Dabkowski, Galkowski, C. Paul Rogers, Cai, Freeman, Lewin, Hurak, Kummert   +7 more
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Iterative learning control design based on feedback linearization and nonlinear repetitive process stability theory

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, Julia Emelianova, Mikhail Emelianov, Krzysztof Gałkowski, Eric Rogers   +4 more
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Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning

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, Jingming Shi, Pedro Borlido, Liming Chen, Silvana Botti, Miguel A. L. Marques   +5 more
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Iterative learning control for discrete linear systems with Zero Markov parameters using repetitive process stability theory

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 ...
Łukasz Hładowski, Krzysztof Gałkowski, Eric Rogers, Zhonglun Cai, Christopher Freeman, P. L. Lewin   +5 more
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Improvement of Power Systems Stability Using a New Learning Algorithm Based on Lyapunov Theory for Neural Network

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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