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Description: 经典的SOM人工神经网络例子源码-classic example ANN FOSS
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Description: 经典的SOM人工神经网络例子源码.zip 经典的SOM人工神经网络例子源码.zip-classic example of artificial neural network source. Zip SOM classic example of artificial neural network source. Zip
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Size: 101376 |
Author: 周正 |
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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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Author: 余建波 |
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Description: SOM神经网络，可以进行特征提取和模式分类，特别是特征维数较多的情况。-SOM neural network, can feature extraction and classification, in particular characteristic dimension of more.
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Size: 1024 |
Author: 周刚 |
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Description: 一个java的小程序,用SOM网络解决的是TSP问题.问题的大小可以自由变化,随机生成权值-a small program that will solve the SOM is TSP. The size of the problem can freely change randomly generated weights
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Size: 26624 |
Author: 于帆 |
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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: 神经网络中的SOM算法的C语言程序实现,可以对初学者了解神经网络有帮助.-SOM neural network algorithm in the C language program can be for beginners understand the neural network has to help.
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Size: 89088 |
Author: yang |
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Description: 采用神经网络中的SOM网络，对数据进行分类-The use of neural networks in the SOM network, the data classification
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Size: 1024 |
Author: 余洁 |
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Description: 关于SOM神经网络算法源代码，内含编译后的EXE程序-SOM neural network algorithm on the source code, containing the compiled EXE program
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Size: 90112 |
Author: cui |
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Description: SOM神经网络算法源程序，同时提供算法的演示源程序。-SOM neural network algorithm source code, while providing algorithm demo source code.
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Size: 97280 |
Author: yxy |
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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: som算法 组织神经网络用于图像分割matlab实现-som-organizing neural network algorithm for image segmentation matlab achieve
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Size: 1024 |
Author: tk |
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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: 分别列出BP网络与som网络的代码，通过运行后可以看出同一种问题在不同网络下的处理差别。-A breakdown of BP network and som network code, by running after the same kind of problem can be seen in the different networks of processing differences.
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Size: 2048 |
Author: 王宇飞 |
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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: Basic library that implements Kohonen s SOM and its learning. Lanuage: C# (.Net 3.5 Framework)
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Size: 35840 |
Author: Larrr |
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Description: 基于SOM的突然分类源码，自编代码希望对大家有所帮助-SOM-based classification of a sudden source, self-compiled code that you want to help
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Size: 766976 |
Author: 刘凯 |
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Description: som matlab code som matlab code-som matlab code som matlab code som matlab code
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Size: 2048 |
Author: kururu |
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Description: MATLAB的自组织映射神经网络源码和相应的实验数据-MATLAB' s self-organizing map neural network source code and the corresponding experimental data
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Size: 1024 |
Author: ccccc |
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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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