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A Comprehensive Benchmark of Neural Networks for System Identification

Abstract : This paper compares a wide variety of neural network architectures applied in the context of black-box modeling for robotics and control. We compare six different architectural concepts and four activation functions, with over three hundred different models. Those models were applied to three robotics datasets to show the differences in performance between the architectures along with their limitations.
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Preprints, Working Papers, ...
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Submitted on : Wednesday, September 4, 2019 - 10:34:34 AM
Last modification on : Wednesday, November 3, 2021 - 8:36:09 AM
Long-term archiving on: : Thursday, February 6, 2020 - 4:15:58 AM


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  • HAL Id : hal-02278102, version 1


Antoine Richard, Antoine Mahé, Cedric Pradalier, Offer Rozenstein, Matthieu Geist. A Comprehensive Benchmark of Neural Networks for System Identification. 2019. ⟨hal-02278102⟩



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