Abstract:In this work, we propose a novel approach to the data‐driven prediction of vibration responses of nonlinear systems. The main idea is based on Autoregressive Neural Networks (ARNN) to model the nonlinear transfer behaviour between an external excitation and the system response. We propose an autoregressive network architecture with embedded symmetry using bias‐free tanh activation and guarantee Input‐to‐State‐Stability (ISS) by enforcing a special penalty term to the weights. The resulting training procedure i… Show more
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