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In several studies, hybrid neural networks have proven to be more robustagainst noisy input data compared to plain data driven neural networks. Weconsider the task of estimating parameters of a mechanical vehicle model basedon acceleration profiles. We introduce a convolutional neural networkarchitecture that is capable to predict the parameters for a family of vehiclemodels that differ in the unknown parameters. We introduce a convolutionalneural network architecture that given sequential data predicts the parametersof the underlying data's dynamics. This network is trained with two objectivefunctions. The first one constitutes a more naive approach that assumes thatthe true parameters are known. The second objective incorporates the knowledgeof the underlying dynamics and is therefore considered as hybrid approach. Weshow that in terms of robustness, the latter outperforms the first objective onnoisy input data.

A Hybrid Objective Function for Robustness of Artificial Neural Networks -- Estimation of Parameters in a Mechanical System