车内噪声声品质的支持向量机预测

对多元线性回归、神经网络和支持向量机的三个预测模型进行了研究。以车内噪声为例,建立了基于以上三种方法的车内噪声声品质预测模型,并采用留一法交叉检验作比较,所构建的支持向量机模型预测精度高于其他两种方法。实验结果同时也表明,支持向量计算法具有较强的稳健性和良好的泛化能力,能够用于车内噪声声品质的预测。

Interiror sound quality forecast for veheicles based on support vector machine

In this paper the t he multiple linear regression model, neural network forecasting model and support vector machine(SVM) forecasting model are proposed. Take vehicle interior noise for example, established vehicle interior sound quality predict model based on the above three methods. The model out puts are cross –validated by the leave-one-out method and compared with each other. The results indicated that the prediction accuracy of SVM is better than the other two methods. The experiment indicates that SVM possess better robustness and generalization capability, which is the best method to predict vehicle interior sound quality.