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pro vyhledávání: '"Emilio, T"'
Efficient global optimization is a widely used method for optimizing expensive black-box functions such as tuning hyperparameter, and designing new material, etc. Despite its popularity, less attention has been paid to analyzing the inherent hardness
Externí odkaz:
http://arxiv.org/abs/2209.09655
This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning.
Externí odkaz:
http://arxiv.org/abs/2205.15703
Autor:
Maddalena, Emilio T., Muller, Silvio A., Santos, Rafael M. dos, Salzmann, Christophe, Jones, Colin N.
Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical
Externí odkaz:
http://arxiv.org/abs/2112.07323
Autor:
Marina Leardini-Tristão, Giulia Andrade, Celina Garcia, Patrícia A. Reis, Millena Lourenço, Emilio T. S. Moreira, Flavia R. S. Lima, Hugo C. Castro-Faria-Neto, Eduardo Tibirica, Vanessa Estato
Publikováno v:
Journal of Neuroinflammation, Vol 21, Iss 1, Pp 1-2 (2024)
Externí odkaz:
https://doaj.org/article/e87407d9219e4fd6a2b8512d3a47d60e
The problem of establishing out-of-sample bounds for the values of an unkonwn ground-truth function is considered. Kernels and their associated Hilbert spaces are the main formalism employed herein along with an observational model where outputs are
Externí odkaz:
http://arxiv.org/abs/2104.09582
We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of
Externí odkaz:
http://arxiv.org/abs/2011.11303
Autor:
Rosolia, Ugo, Lian, Yingzhao, Maddalena, Emilio T., Ferrari-Trecate, Giancarlo, Jones, Colin N.
In this technical note we analyse the performance improvement and optimality properties of the Learning Model Predictive Control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop traje
Externí odkaz:
http://arxiv.org/abs/2010.15153
We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and $\varepsilon$-support vector regression. By assuming the ground-truth function b
Externí odkaz:
http://arxiv.org/abs/2008.04005
Autor:
Maddalena, Emilio T., Jones, Colin N.
Although it is known that having accurate Lipschitz estimates is essential for certain models to deliver good predictive performance, refining this constant in practice can be a difficult task especially when the input dimension is high. In this work
Externí odkaz:
http://arxiv.org/abs/2003.09870
Autor:
Maddalena, Emilio T., Müller, Silvio A., dos Santos, Rafael M., Salzmann, Christophe, Jones, Colin N.
Publikováno v:
In Energy & Buildings 15 September 2022 271