基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。

Research on Sound Quality Prediction Model of Automobile Wind Buffeting Noise Based on GA-BP

At present, the optimization research on wind buffeting noise of automobile mainly uses sound pressure level (SPL) as a single evaluation index, which cannot fully reflect the physical properties of noise, nor can it consider the subjective cognitive process of human ear to noise. The sound quality is introduced to evaluate the wind buffeting noise accurately. Firstly, the large eddy simulation (LES) is used to perform numerical simulation on the wind buffeting noise, whose accuracy can be judged according to the actual vehicle road test. Furthermore, on the basis of the numerical simulation results of wind buffeting noise, the objective and subjective evaluation of sound quality are carried out. The BP neural network prediction model of sound quality is established by integrating with sound quality objective parameters and subjective evaluation of sound quality. Finally, genetic algorithm (GA) is introduced to optimize the structural parameters of BP neural network, and a GA-BP prediction model of sound quality is established. The research results show that GA-BP sound quality prediction model is superior to BP neural network prediction model in training speed and prediction accuracy. The prediction model is based on the subjective and objective evaluation results of sound quality, and its predictive value can replace the traditional sound pressure level evaluation index and provide more accurate and reasonable evaluation for wind buffeting noise.