Results 11 to 20 of about 5,534,457 (290)

Toward robust and scalable deep spiking reinforcement learning

Frontiers in Neurorobotics, 2023
Deep reinforcement learning (DRL) combines reinforcement learning algorithms with deep neural networks (DNNs). Spiking neural networks (SNNs) have been shown to be a biologically plausible and energy efficient alternative to DNNs.
Mahmoud Akl, Deniz Ergene, Florian Walter, Alois Knoll   +3 more
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Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles

Frontiers in Robotics and AI, 2021
Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems.
Simen Theie Havenstrøm, Adil Rasheed, Adil Rasheed, Omer San   +3 more
doaj   +1 more source

A Calibration-Free Hybrid Approach Combining SSVEP and EOG for Continuous Control

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it ...
Ximing Mai, Xinjun Sheng, Xiaokang Shu, Yidan Ding, Xiangyang Zhu, Jianjun Meng   +5 more
doaj   +1 more source

Al'brekht's Method in Infinite Dimensions [PDF]

, 2020
In 1961 E. G. Albrekht presented a method for the optimal stabilization of smooth, nonlinear, finite dimensional, continuous time control systems.
Krener, AJ
core   +3 more sources

The Actor-Dueling-Critic Method for Reinforcement Learning

Sensors, 2019
Model-free reinforcement learning is a powerful and efficient machine-learning paradigm which has been generally used in the robotic control domain. In the reinforcement learning setting, the value function method learns policies by maximizing the state ...
Menghao Wu, Yanbin Gao, Alexander Jung, Qiang Zhang, Shitong Du   +4 more
doaj   +1 more source

Hamilton Jacobi Bellman equations in infinite dimensions with quadratic and superquadratic Hamiltonian [PDF]

, 2010
We consider Hamilton Jacobi Bellman equations in an inifinite dimensional Hilbert space, with quadratic (respectively superquadratic) hamiltonian and with continuous (respectively lipschitz continuous) final conditions.
A. Richou, D. Henry, E. Pardoux, E. Pardoux, E. Pardoux, F. Gozzi, F. Gozzi, F. Masiero, F. Masiero, Federica Masiero, G. Da Prato, G. Da Prato, J. M. Lasry, J. P. Aubin, M. Fuhrman, M. Fuhrman, M. Fuhrman, M. Kobylanski, P. Briand, P. Briand, S. Cerrai, S. Cerrai, S. Peszat, W. H. Fleming, X. Bao, Y. Hu   +25 more
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Continuity controlled hybrid automata

The Journal of Logic and Algebraic Programming, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bergstra, J.A., Middelburg, C.A.
openaire   +6 more sources

Immunoprophylaxis of influenza, acute and recurrent respiratory infections during the COVID-19 pandemic

Медицинский совет, 2021
The article notes features of the last epidemic season (2020-2021) in the form of extremely low activity of influenza viruses with SARS-CoV-2 remaining dominant in the etiological structure of acute respiratory viral infections.
A. A. Girina, A. L. Zaplatnikov, E. I. Burtseva, V. I. Svintsitskaya, I. D. Maykova, N. F. Dubovets, N. V. Koroid, I. V. Lepiseva, A. Е. Voskanyan, D. N. Tkacheva, A. D. Panova, S. A. Motorny   +11 more
doaj   +1 more source

Does the motor system need intermittent control? [PDF]

, 2014
Explanation of motor control is dominated by continuous neurophysiological pathways (e.g. trans-cortical, spinal) and the continuous control paradigm.
Brembs, Cisek, Cohen, Cornelis van de Kamp, Diedrichsen, Dux, Frank, Gawthrop, Gawthrop, Gawthrop, Gawthrop, Gollee, Haber, Hardwick, Henrik Gollee, Houk, Ian David Loram, Kleinman, Lakie, Levison, Loram, Loram, Loram, Loram, Loram, Loram, Marsden, Martin Lakie, Peter J. Gawthrop, Pruszynski, Redgrave, Redgrave, Ronco, Todorov, Van De Kamp, Van de Kamp, Vince, Yin   +37 more
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Continuous Improvement in Continuous Quality Control [PDF]

Clinical Chemistry, 2016
“Quality is never an accident. It is always the result of intelligent effort.” So observed John Ruskin, Victorian writer and critic of art, architecture, and society (1), unknowingly foreshadowing the emergence of quality science in the 20th century. Statistical QC came from Bell Telephone Laboratories, where Shewhart developed the statistical control ...
openaire   +2 more sources