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self organizing map
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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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The self-organizing map is an artificial neural networks model and algorithm that implememts a characteristic nonliner projection from the high-dimensional space of signal data into a low-dimensional array of neurons in an orderly fashion, which is made by T. Kohonen. The mapping tends to preserve the topological relationships of signal domains. Owing to the order, the image of the signal space tends to manifest clusters of input information and their relationship on the map. The map...

The self-organizing map is an artificial neural networks model and algorithm that implememts a characteristic nonliner projection from the high-dimensional space of signal data into a low-dimensional array of neurons in an orderly fashion, which is made by T. Kohonen. The mapping tends to preserve the topological relationships of signal domains. Owing to the order, the image of the signal space tends to manifest clusters of input information and their relationship on the map. The map can be shown by square shades of gray, so it makes machinery operating conditions be visualized. The map will vary with changes of machinery operating conditions, which helps us to detect and identify the faults . 

自组织映射法是由T.Kohonen提出的一种人工神经网络模式,它能把高维的信息数据以有序方式映射到低维的网络上,形成一种拓扑意义上的有序图。由于其有序性,使得信号空间图成为许多输入信息及其关系的群落图。每个图可用灰度深浅不等的方块阴影图表示出来,这种可视的图形表示了机械运行的状态。人们可以非常直观地从图上判断机械运行状态,从而使得机械运行状态的监测和故障诊断更为简便。

Based on the idea of realizing non-linear transformation with inner product in support vector machine, a new technique, kernel self-organizing maps (KSOM), is presented. By use of the kernel function in the input space, which constructs self-organizing competitive discrimination criterions and adjusts neurons weights in the mapped high-dimensional space, the KSOM method clusters the input data more efficiently. The application of the proposed method in industrial gearbox condition...

Based on the idea of realizing non-linear transformation with inner product in support vector machine, a new technique, kernel self-organizing maps (KSOM), is presented. By use of the kernel function in the input space, which constructs self-organizing competitive discrimination criterions and adjusts neurons weights in the mapped high-dimensional space, the KSOM method clusters the input data more efficiently. The application of the proposed method in industrial gearbox condition monitoring is studied. The analysis results show that using the method the gearbox-operating condition with fatigue crack or broken tooth compared with the normal condition is identified clearly. Moreover, with the trajectory of the image points for the feature data in the output-layer of the network, the variation of gearbox conditions is observed visually, and the development of gearbox early-stage failures is monitored in time. Compared with the standard SOM, the proposed method performs better.

基于支持向量机用内积运算实现非线性变换的思想 ,提出基于核函数的自组织映射方法。该方法借助于核函数在原输入空间构造非线性映像空间的自组织竞争评价函数及权值调整方法 ,从而更好地解决输入向量的聚类问题。研究该方法在齿轮箱状态监测中的应用 ,分析表明 ,它可以清楚地将齿轮箱不同状态区分开 ,并且特征数据映象点在网络输出层的轨迹变化趋势直观反映齿轮箱工作状态的变化 ,便于及时监测到齿轮箱早期故障及变化趋势 ,与标准self or ganizingmaps(SOM)相比 ,该方法性能更稳定 ,效果更好

A new method, distance mapping, is presented in order to visualize the trained results by self-organizing maps (SOM) apparently. By means of similarities evaluated based on Euclidean distances between input vectors and output neurons weights combining with the distribution of fixed lattices in the network, high-dimensional input vectors are projected into a two-dimensional space. SOM is employed in fault recognition and condition monitoring of gearbox combining with the proposed visualizing...

A new method, distance mapping, is presented in order to visualize the trained results by self-organizing maps (SOM) apparently. By means of similarities evaluated based on Euclidean distances between input vectors and output neurons weights combining with the distribution of fixed lattices in the network, high-dimensional input vectors are projected into a two-dimensional space. SOM is employed in fault recognition and condition monitoring of gearbox combining with the proposed visualizing technique. It is proved that feature points under gear normal, tooth cracked and tooth broken conditions are mapped into different areas on two-dimensional space more clearly by distance mapping than U-matrix method, which helps distinguish gearbox conditions correctly. With the trace of the image points for gear feature data on the plane, the variation of gearbox conditions is observed visually, and furthermore, early gear failures occurrence and its varying trend is monitored in time.

提出了一种自组织映射网络训练结果的可视化方法——距离映射祛,该方法通过计算出竞争层神经元权矢量与输入模式的相似度,并综合考虑神经元的网格分布,把输入矢量降维映射到二维平面。结合该方法研究了自组织映射网络在齿轮箱故障识别和状态监测中的应用。与U—矩阵法相比,该方法能更加清楚地将齿轮正常、裂纹和断齿状态的特征数据映射到二维平面的不同区域,将齿轮箱状态聚类分开,特征数据在平面上的映像点轨迹变化趋势直观反映了齿轮箱工作状态的变化,便于及时监测识别出齿轮的早期故障及其变化趋势。

 
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