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Abstract
Aiming at the deficiencies in the existing research on road roughness recognition based on neural networks, the road roughness and 16 vehicle response data are simulated based on the filtered white noise model and the smoothness seven-degree-of-freedom model, NARX neural network is built to identify road roughness. The coefficient of determination and the root mean square error are introduced as the evaluation indicators of the model, the MIV method is used to evaluate and screen each input response. Research shows that MIV method improves the performance of NARX neural networks, MIV-NARX can effectively identify road roughness.
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Details
1 Department of Vehicles, Wuhan University of Technology, Wuhan, Hubei, China