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self organizing map
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  自组织神经网络
    According to self organizing map principle, a visualized topology map model for radar system effectiveness analysis based on information fusion of multiple parameters is built, and the effectiveness of different airborne radar is analyzed through the topology map obtained from the model.
    根据自组织神经网络原理 ,构建了基于多参数信息融合的雷达系统效能可视拓扑映射图分析模型 ,并根据模型学习获得的拓扑映射图对机载雷达效能进行了分析。
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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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According to self organizing map principle, a visualized topology map model for radar system effectiveness analysis based on information fusion of multiple parameters is built, and the effectiveness of different airborne radar is analyzed through the topology map obtained from the model. Through the topology ordering map and single parameter gray scale image from the model, the characteristics of detection capability, parameter correlation, parameter variation tendency...

According to self organizing map principle, a visualized topology map model for radar system effectiveness analysis based on information fusion of multiple parameters is built, and the effectiveness of different airborne radar is analyzed through the topology map obtained from the model. Through the topology ordering map and single parameter gray scale image from the model, the characteristics of detection capability, parameter correlation, parameter variation tendency of different type of radar can be observed clearly, and similarity prediction for the effectiveness of the other radars can be carried out. Compared with traditional methods, this paper provides a valid and rational way for effectiveness analysis.

根据自组织神经网络原理 ,构建了基于多参数信息融合的雷达系统效能可视拓扑映射图分析模型 ,并根据模型学习获得的拓扑映射图对机载雷达效能进行了分析。通过效能模型的拓扑映射图和单参数灰度图 ,可以清晰地观察到不同型号雷达的探测能力特征、参数相关性特征、参数变化趋势 ,还可对其它型号雷达效能进行相似性预测 ;同时 ,为装备效能分析提供了一种新的有效的方法。

 
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