基于BP神经网络多类分类的湍流目标探测

传统的湍流探测方法需要利用经验公式和参数化模型等,公式与模型的正确性大大影响了探测的准确性。基于反向传播(back propagation,BP)神经网络多类分类方法的气象湍流目标探测算法无需借助经验公式和参数化模型,利用神经网络的分类功能,仅通过对大量数据的学习可有效地确立雷达回波与湍流强度之间的关系。仿真结果表明,所提出的方法在进行湍流强度时有可忽略、轻微、中度、强4个等级分类,有良好的准确性,在进行湍流2个等级分类,即判定湍流有无时,准确率将大大提高。理论和实践结果表明,所提出的方法可以有效地进行湍流目标探测。

Turbulence target detection based on BP neural network multi level classification

Traditional turbulence detection methods need to use empirical formulas and parameterized models, and the correctness of the formulas and models greatly influence the accuracy of the detection. The turbulence target detection algorithm based on back propagation (BP) neural network multi-level classification method does not need to use empirical formulas and parameterized models, with the utilization of neural network classification function, it can effectively establish the relationship between radar echoes and the intensity of turbulence only through the study of large amounts of data. The simulation results show that the proposed method has a good accuracy in the classification of four turbulence intensity grades, namely, negligible, mild, moderate, strong. And the accuracy will be greatly improved in the classification of two turbulence intensity levels, namely to determine whether there is turbulence or not. The theory and practice results show that the proposed method can effectively detect the turbulence target.