Results 261 to 270 of about 13,204,286 (295)

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

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, Silvana Botti, MIGUEL A L Marques   +2 more
exaly   +3 more sources

Stability Certificates for Neural Network Learning-based Controllers using Robust Control Theory

2021 American Control Conference (ACC), 2021
Providing stability guarantees for controllers that use neural networks can be challenging. Robust control theoretic tools are used to derive a framework for providing nominal stability guarantees – stability guarantees for a known nominal system – controlled by a learning-based neural network controller.
Rolf Findeisen, Richard D Braatz, Hoang Hai Nguyen   +2 more
exaly   +3 more sources

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, Pedro Borlido, Liming Chen   +2 more
exaly   +2 more sources

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, Eric Rogers, Krzysztof Galkowski   +2 more
exaly   +3 more sources

Further results on dynamic iterative learning control law design using repetitive process stability theory

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
Eric Rogers, Lukasz Hladowski, Krzysztof Galkowski   +2 more
exaly   +3 more sources

Deep Lyapunov Learning: Embedding the Lyapunov Stability Theory in Interpretable Neural Networks for Transient Stability Assessment

IEEE Transactions on Power Systems
Tao Ding, Rudai Yan
exaly   +2 more sources

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.
Rakhlin, Alexander, Mukherjee, Sayan, Poggio, Tomaso   +2 more
openaire   +2 more sources

Stability theory of universal learning network

1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929), 2002
Higher order derivatives of the universal learning network (ULN) has been previously derived by forward and backward propagation computing methods, which can model and control the large scale complicated systems such as industrial plants, economic, social and life phenomena. In this paper, a new concept of nth order asymptotic orbital stability for the
K. Hirasawa, M. Ohbayashi, M. Koga, N. Kusumi   +3 more
openaire   +1 more source

A NOTE ON STABILITY OF ERROR BOUNDS IN STATISTICAL LEARNING THEORY

Analysis and Applications, 2011
We consider a wide class of error bounds developed in the context of statistical learning theory which are expressed in terms of functionals of the regression function, for instance, its norm in a reproducing kernel Hilbert space or other functional space.
Li, Ming, Caponnetto, Andrea
openaire   +2 more sources

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.
Pawel Grzegorz Dabkowski, Krzysztof Galkowski, Eric Rogers, Zhonglun Cai, Christopher T. Freeman, Paul L. Lewin, Zdenek Hurák, Anton Kummert   +7 more
openaire   +1 more source