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. Platform: |
Size: 1024 |
Author:周刚 |
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Description: 一个可以运行的som神经网络程序，可以任意输入输入和输出向量数。用于分类和测试-one can run the som neural network program, which could be imported input and output vectors of a few. For the classification and testing Platform: |
Size: 23552 |
Author:周君 |
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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. Platform: |
Size: 89088 |
Author:yang |
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Description: SOM神经网络分类程序，使用matlab编写-SOM neural network classification procedures, the use of matlab to prepare Platform: |
Size: 5120 |
Author:刘杰 |
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Description: classic SOM program for using with Matlab Neural network toolbox
useful for data classification etc. Platform: |
Size: 140288 |
Author:张一 |
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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 Platform: |
Size: 47104 |
Author:leidan |
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