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Description: 一个自组织映射的很好的程序，其中包含4个演示程序，使用者也可以在此基础上进行开发，很方便的。-a Self-Organizing Map of good procedures, which include four demo program, users can also conducted on the basis of this development, is very convenient.
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Size: 359424 |
Author: 王晓 |
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Description: 一个MATALAB写的SOM神经网络原代码程序-a MATALAB write SOM neural network source procedures!
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Size: 1024 |
Author: 余建波 |
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Description: matlab编写的训练som自组织特征映射神经网络的源代码。-Matlab prepared by the training som self-organizing feature map neural network source.
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Size: 1024 |
Author: 唐研 |
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Description: 基于MATLAB的SOM神经网络设计源程序-MATLAB-based SOM neural network design source
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Size: 4096 |
Author: lihuan |
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Description: 一个用C编写的SOM（自组织特征映射）神经网络源代码。-a C prepared by the SOM (self-organizing feature map) neural network source code.
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Size: 89088 |
Author: ppddt2005 |
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Description: 竞争学习指同一神经元层次上各个神经元相互之间进行竞争，竞争胜利的神经元修改与其相联的连接权值。竞争学习是一种无监督学习。在无监督学习中，只向网络提供一些学习样本，而不提供理想的输出。网络根据输入样本进行自组织，并将其划分到相应的模式类中。
-Competitive learning refers to the same level in all neurons neurons compete with each other, competition victory neuronal modifications associated with its connection weights. Competitive learning is an unsupervised learning. In unsupervised learning, only learning to network with some samples, rather than provide an ideal output. Network in accordance with the importation of samples for self-organization, and its division into the corresponding model class.
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Size: 2048 |
Author: liyan |
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Description: 一个基于自组织神经网络的图像聚类和融合源程序-A self-organizing neural network-based image clustering and integration of source
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Size: 1024 |
Author: 何苦 |
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Description: 竞争学习指同一神经元层次上各个神经元相互之间进行竞争，竞争胜利的神经元修改与其相联的连接权值。竞争学习是一种无监督学习。在无监督学习中，只向网络提供一些学习样本，而不提供理想的输出。网络根据输入样本进行自组织，并将其划分到相应的模式类中。
-Competitive learning refers to the same level in all neurons neurons compete with each other, competition victory neuronal modifications associated with its connection weights. Competitive learning is an unsupervised learning. In unsupervised learning, only learning to network with some samples, rather than provide an ideal output. Network in accordance with the importation of samples for self-organization, and its division into the corresponding model class.
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Size: 1024 |
Author: 于鹏 |
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Description: Kohonen的SOM软件包，matlab里最好的som软件-Kohonen s SOM package, matlab best som software
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Size: 1543168 |
Author: lance |
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Description: The source code of SOM (Self Organizing Map)
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Size: 38912 |
Author: jantima |
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Description: 神经网络中自组织特征映射模型算法的源程序，属于无导师学习！-Neural network model self-organizing feature map algorithm source, belonging to no mentor to learn!
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Size: 90112 |
Author: 许波 |
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Description: 使用SOM(Self-Organization Map)進行資料分群，以載入座標點資料集及並以TChart分群顯示為例.-Using SOM (Self-Organization Map) data clustering to include the coordinates of point data sets and to show as an example TChart grouping.
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Size: 354304 |
Author: toto |
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Description: 基于自组织特征映射（SOM）的系统辨识算法实现-Based on self-organizing feature map (SOM) of the system identification algorithm
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Size: 2048 |
Author: JW |
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Description: VC++实现神经网络SOM（自组织特征映射网）-VC++ Realization of neural network SOM (self-organizing feature map network)
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Size: 3072 |
Author: sugar |
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Description: MRI Brain Tumour Classification - SOM ( Self Organized Map)-MRI Brain Tumour Classification- SOM ( Self Organized Map)
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Size: 371712 |
Author: sunda |
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Description: SOM自组织神经网络的C语言源码，可以通过该程序实现无监督的聚类。-SOM self-organizing neural network C-language source code
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Size: 3072 |
Author: Joyce |
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Description: Kohonen article of self-organizing maps, published in 1982 in IEEE. It contain general ideas about self-organizing neural networks.
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Size: 1497088 |
Author: luxury_x |
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Description: SOM神经网络（自组织特征映射神经网络）是一种无导师神经网路。网络的拓扑结构是由一个输入层与一个输出层构成。输入层的节点数即为输入样本的维数，其中每一节点代表输入样本中的一个分量。输出层节点排列结构是二维阵列。输入层X中的每个节点均与输出层Y每个神经元节点通过一权值(权矢量为W)相连接，这样每个输出层节点均对应于一个连接权矢量。
自组织特征映射的基本原理是，当某类模式输入时，其输出层某一节点得到最大刺激而获胜，获胜节点周围的一些节点因侧向作用也受到较大刺激。这时网络进行一次学习操作，获胜节点及其周围节点的连接权矢量向输入模式的方向作相应的修正。当输入模式类别发生变化时，二维平面上的获胜节点也从原来节点转移到其它节点。这样，网络通过自组织方式用大量训练样本数据来调整网络的连接权值，最后使得网络输出层特征图能够反映样本数据的分布情况。根据SOM网络的输出状况，不仅能判断输入模式所属的类别，使输出节点代表某类模式，而且能够得到整个数据区域的分布情况，即从样本数据得到所有数据的分布特征。 -SOM neural network (self-organizing feature map neural network) is an unsupervised neural network. Network topology is an input layer and an output layer. Input layer nodes is the input dimension of the sample, each node represents a component input samples. Output layer nodes are arranged in two-dimensional array structure. X in the input layer and output layer each node of each neuron node Y by a weight (the weight vector as W) is connected, so that each output layer corresponds to a connection node of the right vector.
Self-organizing feature maps of the basic principle is, when each category of inputs into the model, its output layer one node get the maximum boost and win, Huoshengjiedian around Yixiejiedian Yin Zuo Yong Ye Shoudaojiaotai lateral stimulation. Then a learning network operation, the winner node and surrounding nodes in the right direction vector to the input mode to make consequential amendments. When the input mode type changes, the two-dimensional plane of the wi
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Size: 47104 |
Author: leidan |
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Description: ANN Simulink/Matlab. Constructing Kohonen Self-Organizing-Map (SOM)
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Size: 150528 |
Author: Pavigo |
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Description: 完全用C++实现的SOM(自组织网络映射)算法，可以用于实际工作和学习中-Entirely in C++ implementation of the SOM (self-organizing network mapping) algorithm, can be used for practical work and learning
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Size: 55296 |
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