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self organizing map
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  自组织特征映射
    Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously.
    自组织特征映射是一种人工神经网络方法 ,可以同时实现模式识别和数据分类。
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  self organizing map
In fact, a Self Organizing Map (SOM), combined with multiple recurrent neural networks (RNN) has been trained to predict the components of noisy and large data set.
      
The training set is fed to a self organizing map neural network to cluster the measurements.
      
The Self Organizing Map (SOM) algorithm has been utilized, with much success, in a variety of applications for the automatic organization of full-text document collections.
      
In this paper we study the sensitivity of the Self Organizing Map to several parameters in the context of the one-pass adaptive computation of cluster representatives over non-stationary data.
      
We only need to replace normal E-step with the modified E-step presented here to obtain a self-organizing map version of the given mixture model.
      
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Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously. SOM is able to convert complex nonlinear statistical relationships of high dimensional data into simple geometric relationships. In this article, we investigate the variationpatterns of precipitation intensity and maximum water level in Poyang Lake valley with self organizing map. Then the cause of flood disaster is discussed according to...

Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously. SOM is able to convert complex nonlinear statistical relationships of high dimensional data into simple geometric relationships. In this article, we investigate the variationpatterns of precipitation intensity and maximum water level in Poyang Lake valley with self organizing map. Then the cause of flood disaster is discussed according to the result of calculation.

自组织特征映射是一种人工神经网络方法 ,可以同时实现模式识别和数据分类。它可以将高维数据间复杂、非线性的统计关系转换为低维形式中简单的几何关系。该文利用自组织特征映射方法对鄱阳湖地区月降水强度和月高水位变化模式进行分析 ,对鄱阳湖地区洪水灾害形成原因进行归纳

 
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