Searched for: subject%3A%22Feedforward%255C+neural%255C+networks%22
(1 - 6 of 6)
document
Overwater, R.W.J. (author), Babaie, M. (author), Sebastiano, F. (author)
Quantum error correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electronic back-end. Decoders employing neural networks (NN) are well-suited for this task but their...
journal article 2022
document
Kon, Johan (author), Bruijnen, Dennis (author), van de Wijdeven, Jeroen (author), Heertjes, Marcel (author), Oomen, T.A.E. (author)
Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model is complemented by a neural network. The neural network output in...
conference paper 2022
document
Wagih, Hassan (author), Osman, M.E.A. (author), Awad, Mohammed I. (author), Hammad, Sherif (author)
In this paper, an approach for reducing the drift in monocular visual odometry algorithms is proposed based on a feedforward neural network. A visual odometry algorithm computes the incremental motion of the vehicle between the successive camera frames, then integrates these increments to determine the pose of the vehicle. The proposed neural...
conference paper 2022
document
Kon, Johan (author), Bruijnen, Dennis (author), van de Wijdeven, Jeroen (author), Heertjes, Marcel (author), Oomen, T.A.E. (author)
Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and...
conference paper 2022
document
Bosma, Stijn (author)
A repetitive motion system supporting nano meter precision is positioned at high accelerations, which produces a force that disturbs the demanded accuracy requirements. Iterative learning control is used to learn optimal feedforward control signals for the attenuation this disturbance force. The iterative method comes with a limitation, as it...
master thesis 2019
document
De Weerdt, E. (author), Chu, Q.P. (author), Mulder, J.A. (author)
The problem of output optimization within a specified input space of neural networks (NNs) with fixed weights is discussed in this paper. The problem is (highly) nonlinear when nonlinear activation functions are used. This global optimization problem is encountered in the reinforcement learning (RL) community. Interval analysis is applied to...
journal article 2009
Searched for: subject%3A%22Feedforward%255C+neural%255C+networks%22
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