基于神经网络法的舱室噪声预测

提出了利用人工神经网络(ANN)预测舱室噪声的方法。针对现有封闭声场模拟方法适用频带范围有限和所需参数过多的不足，结合舱室噪声场的特点，给出了一种可模拟封闭空间内部噪声的BP神经网络算法．进行了仿真实验。结果表明，利用12输入、2个隐含神经元和1输出的ANN模型．可以准确地预测不同大小矩形空间内部的噪声分布情况，而且对于中高频和低频段都具有较高的精度。

On Improving Prediction of Cabin Noise with BPANN Technique

Existing methods for predicting cabin noise appear to have two shortcomings: (1)application frequency range is not wide enough; (2)parameters required are unnecessarily too many. Having had several years of research experience in predicting cabin noise, I formed gradually the idea of overcoming these shortcomings with BPANN(Back Propagation Artificial Neural Network). Instead of several tens of parameters needed by existing methods, BPANN needs only twelve input variables, two hidden variables and one output variable. The data used in training the BPANN algorithm come from two sources:(1) measurements; (2) results predicted by ray-tracing method. Simulation results show preliminarily that BPANN algorithm can predict correctly the sound pressure levels in enclosed spaces and its precision for high frequency band is about the same as that for low frequency band.