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Found 6 papers, 1 papers with code
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.
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control.
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.
Linear Antisymmetric Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have a form of memory where the output from a node at one timestep is fed back as input the next timestep in addition to data from the previous layer.
CASCLIK: CasADi-Based Closed-Loop Inverse Kinematics
A Python module for rapid prototyping of constraint-based closed-loop inverse kinematics controllers is presented.
Robotics
