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首页> 外文期刊>International Journal of Electrical and Computer Engineering >Reflectivity Parameter Extraction From RADAR Images Using Back Propagation Algorithms
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Reflectivity Parameter Extraction From RADAR Images Using Back Propagation Algorithms

机译：使用反向传播算法从RADAR图像中提取反射率参数。

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摘要

Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as Levenberg-Marquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98%.
机译：模式识别自上世纪90年代开始就已被公认为是最有前途的研究领域之一，并已引起许多研究者的关注。多层神经网络基于从输入模式中得出的特征，用于模式识别和分类。从多普勒天气雷达（DWR）图像中提取的反射率信息有助于识别与降水速率密切相关的对流云类型。反射率信息借助颜色植根于DWR图像中，并提供色条以区分不同的反射率信息。人工神经网络根据最大似然估计问题预测颜色。通过比较各种反向传播算法（如Levenberg-Marquardt，共轭梯度和弹性反向传播等），提出了一种用于DWR图像颜色识别的最佳最佳反向传播算法。与标准距离度量相比，使用神经网络的模式识别呈现出更好的结果。可以观察到Levenberg-Marquardt反向传播算法产生的回归值大约为99％，准确度为98％。

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