线性回归和BP神经网络在噪声监测中的应用

噪声监测系统能够自动测量噪声分贝值,并实时处理系统监测到的各种声音环境信息,但是在噪声监测系统的实际应用中,噪声的分贝值受到温度、湿度和大气压力等多个因素影响,与实际值存在误差.为了提高噪声的测量精度,必须使用相关技术进行校正,系统采用了线性回归和BP神经网络技术,研究了预测模型的因素和系数,分析了模型中因素的相关性,获得了噪声监测的自动校正模型.从线性回归和BP神经网络自动校正数据的测试效果看,优化了测量数据的容错性并改进了数据校正的精度,使预测模型的判定系数R2的值有了较大提升.

Application of Linear Regression and BP Neural Network in Noise Monitoring

Noise monitoring systems can automatically measure decibel level and process various sound environment information in real time. However, in their practical application, the noise decibel is affected by many factors such as temperature, humidity and atmospheric pressure, which leads to the errors between measured and actual values. In view of this, the correction based on relevant technologies becomes a necessity for the accuracy improvement of noise measurement. This study adopts linear regression and Back Propagation (BP) neural network to investigate the factors and coefficients of the prediction model and analyzes the correlation of factors in the model. As a result, the automatic correction model of noise monitoring is obtained. The test effect of automatic data correction by linear regression and BP neural network indicates that the fault tolerance of measurement data is optimized and the accuracy of data correction is improved. Further, the determination coefficient (R²) of the prediction model is greatly increased.