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Deep active learning for nonlinear system identification
This simulated dataset can be used in a static deep active learning acquisition scheme referred to as global explorations.
Sparse neural networks with skip-connections for identification of aluminum electrolysis cell
Neural networks are rapidly gaining interest in nonlinear system identification due to the model's ability to capture complex input-output relations directly from data.
Sparse deep neural networks for modeling aluminum electrolysis dynamics
In this work, we demonstrate the value of sparse regularization techniques to significantly reduce the model complexity.
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