Identification of Road Surface Power Spectrum Density Based on a New Cubic Spline Weight Neural Network

Aiming at the shortcomings appearing in the BP neural network method for obtaining the road surface power spectrum density, the paper applies a new cubic spline weight function neural network (CSWFNN) to the identification of road surface power spectrum density. First, a vibration model with four freedoms is established to simulate the actual vehicles. Subsequently, we deduce the nonlinear relationship between power spectrum density of the response and that of the excitation, and then acquire some input and output data. Next, a CSWFNN is built and trained until converges. In the end, the paper analyses the errors of CSWFNN in the identification of road surface power spectrum density. The results show that the proposed method in the paper successfully overcomes the deficiencies of BP neural network method, and consequently enhances the identification precision of road surface power spectrum density.