Neural identification of non-linear dynamic structures

Neural networks are applied to the identification of non-linear structural dynamic systems. Two complementary problems inspired from customer surveys are successively considered. Each of them calls for a different neural appr oach. First, the mass of the system is identified based on acceleration rec ordings. Statistical experiments are carried out to simultaneously characte rize optimal pre-processing of the accelerations and optimal neural network models. It is found that key features for mass identification are the four th statistical moment and the normalized power spectral density of the acce leration. Second, two architectures of recurrent neural networks, an autore gressive and a state-space model, are derived and tested for dynamic simula tions, showing higher robustness of the autoregressive form. Discussion is first based on a non-linear two-degree-of-freedom problem. Neural identific ation is then used to calculate the load from seven acceleration measuremen ts on a car. Eighty three per cent of network estimations show below 5% err or. (C) 2001 Academic Press.