用反向传播神经网络识别强降水雷达回波反射率

Jing Wang, Yuchun Gao, Yiyang Xiong, M. Cheng, Shuai Zhu
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引用次数: 0

摘要

本文采用人工神经网络(ANN)的反向传播(BP)模型对长江-淮河地区汛期强降水雷达回波反射率进行了识别。该网络于2001年在同一地区的一次降水过程中得到应用。结果表明:单隐层BP神经网络能够以较高的成功率识别目标雷达回波。同时也验证了网络的性能受以下因素的影响:训练样本的质量和输入顺序、隐藏层的框架和学习率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Severe Precipitation Radar Echo Reflectivity with Back-Propagation ANN
In this thesis, the radar echo reflectivity of severe precipitation in the flood season of Changjiang-Huaihe area was identified by a Back-Propagation (BP) Model of Artificial Neural Network (ANN). The trained network was applied in a precipitation progress in the same area in 2001. The results illustrate that: the single hide-layer BP ANN can be used to identify the target radar echo at a high succeed rate. It is also validated that the performance of the network is influenced by following factors: the quality and input sequence of the training sample, the framework of hide layer and the learning rate.
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