Data Driven prediction of forced nonlinear vibrations using stabilised Autoregressive Neural Networks
نویسندگان
چکیده
In this work, we propose a novel approach to the data-driven prediction of vibration responses nonlinear systems. The main idea is based on Autoregressive Neural Networks (ARNN) model transfer behaviour between an external excitation and system response. We autoregressive network architecture with embedded symmetry using bias-free tanh activation guarantee Input-to-State-Stability (ISS) by enforcing special penalty term weights. resulting training procedure analysed for example DUFFING oscillator white noise excitation. BAYESian optimisation, it found that beyond input-to-state-stability, stabilising also decreases sensitivity respect other parameters compared classical techniques. Furthermore, show stabilised ARNN able give excellent approximations response wide range intensities. contrast, linear models, such as models exogenous input (ARX) in time domain or functions frequency domain, will only find some approximation. particular, construction, they not be capture effects arbitrary amplitudes levels.
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ژورنال
عنوان ژورنال: Proceedings in applied mathematics & mechanics
سال: 2023
ISSN: ['1617-7061']
DOI: https://doi.org/10.1002/pamm.202200318