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1. 发明授权

US5796611A Weather forecast apparatus and method based on recognition of echo patterns of radar images 失效
标题翻译：基于识别雷达图像回波模式的天气预报装置和方法
公开(公告)号：US5796611A
公开(公告)日：1998-08-18
申请号：US538723
申请日：1995-10-03
申请人： Keihiro Ochiai , Hideto Suzuki , Noboru Sonehara
发明人： Keihiro Ochiai , Hideto Suzuki , Noboru Sonehara
IPC分类号： G01S7/41 , G01S13/95 , G01W1/10 , G06F17/10
CPC分类号： G01W1/10 , G01S13/95 , G01S7/417 , Y10S706/931
摘要： The present invention provides a weather forecast apparatus and a method for the same, to systematically classify a measured radar image based on results of pattern classification of past radar images so as to use the classified radar image. In the present invention, rapid forecasting is possible by making the FNN model previously learn based on data of each class (and indexes for forecast times) obtained by classification of past weather data for every resembling pattern. In addition, a calculation procedure for improving the classifying ability of patterns can be established by varying the procedure for calculating feature quantities with regard to the radar image by using the learning of the TNN model. Furthermore, systematic classification of a pre-learned image can be realized by performing self organization with regard to compound feature quantities extracted from a radar image in the PNN model, a typical example of which is a competitive learning model.
摘要翻译：本发明提供一种天气预报装置及其方法，基于过去的雷达图像的模式分类的结果对测量的雷达图像进行系统分类，以便使用分类的雷达图像。在本发明中，通过使FNN模型先前基于通过对于每个类似模式的过去天气数据的分类获得的每个类别（和预测时间的索引）的数据来进行快速预测是可能的。另外，可以通过使用TNN模型的学习改变用于计算关于雷达图像的特征量的过程来建立用于提高图案分类能力的计算过程。此外，可以通过对PNN模型中的雷达图像提取的复合特征量进行自组织来实现预先学习的图像的系统分类，其典型的例子是竞争性学习模型。

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