Web: http://arxiv.org/abs/2201.12088

Jan. 31, 2022, 2:11 a.m. | Max Bolderman, Mircea Lazar, Hans Butler

cs.LG updates on arXiv.org arxiv.org

Performance of model-based feedforward controllers is typically limited by
the accuracy of the inverse system dynamics model. Physics-guided neural
networks (PGNN), where a known physical model cooperates in parallel with a
neural network, were recently proposed as a method to achieve high accuracy of
the identified inverse dynamics. However, the flexible nature of neural
networks can create overparameterization when employed in parallel with a
physical model, which results in a parameter drift during training. This drift
may result in parameters …

arxiv networks neural neural networks physics training

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