为了研究时间域上风廓线数据的质量控制问题, 采用后向传播神经网络修正灰色算法残差的方法(BP-GM), 进行了理论分析和实验验证。使用反向传播神经网络训练历史风廓线数据的灰色残差, 取得了风廓线质量控制数据。结果表明, 当相对误差和后验差比值越小、精度越接近1时, 质量控制效果越好;BP-GM法能有效地降低风廓线数据控制残差, 提高精度。这一结果对风廓线质量控制是有帮助的。

In order to study quality control problem of wind profile data in time domain, a method of neural network was used to correct residual error of grey algorithm. Back propagation neural network was used to train grey residual error of historical wind profile data, and the quality control data of wind profile was obtained. After theoretical analysis and experimental verification, the results show that the algorithm can effectively reduce residual error and improve the accuracy of wind profile data. The result is helpful for quality control of wind profile.