Description: 神经网络源码可应用于遥感图像分类，包括bp、kohonen等方法-neural network source can be used in remote sensing image classification, including bp, Kohonen methods Platform: |
Size: 3882 |
Author:刘武 |
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Description: 神经网络源码可应用于遥感图像分类，包括bp、kohonen等方法-neural network source can be used in remote sensing image classification, including bp, Kohonen methods Platform: |
Size: 4096 |
Author:刘武 |
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Description: Kohonen and CPANN toolbox是开发的一个MATLAB工具箱，用来对数据结构进行研究和分类。-The Kohonen and CPANN toolbox is a collection of MATLAB modules for developing Kohonen Maps and Counterpropagation Artificial Neural networs (CPANNs). These are well known neural networks aimed to the study of data structure (Kohonen Maps) and to the data classification (CPANNs).
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Size: 300032 |
Author:宁宁 |
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Description: application som kohonen , pour une classification de couleur et voir l application de l algorithme Platform: |
Size: 805888 |
Author:chergui |
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Description: application som kohonen , pour une classification de couleur et voir l application de l algorithme Platform: |
Size: 233472 |
Author:chergui |
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Description: Neural Network Based Clustering
using Self Organizing Map (SOM) in Excel
Here is a small tool in Excel using which you can find clusters in your data set. The tool uses Self Organizing Maps (SOM) - originally proposed by T.Kohonen as the method for clustering.
* Neural Network based Clustering tool in Excel (209 KB in Zipped format. 947 KB when unzipped.)
Inside the downloaded zip file, you will find the Excel file containing the application. Before running it, I suggest that you go through the ReadMe worksheet. It contains brief instructions on how to run the tool.
If you are interested in building Prediction and Classification models in Excel using Feedforward-Backpropagation Neural Network, here are two small Excel based tools for you. Also, if you are interested in Tree based Classification models, here is a Tree based classifier in Excel.
-Neural Network Based Clustering
using Self Organizing Map (SOM) in Excel
Here is a small tool in Excel using which you can find clusters in your data set. The tool uses Self Organizing Maps (SOM)- originally proposed by T.Kohonen as the method for clustering.
* Neural Network based Clustering tool in Excel (209 KB in Zipped format. 947 KB when unzipped.)
Inside the downloaded zip file, you will find the Excel file containing the application. Before running it, I suggest that you go through the ReadMe worksheet. It contains brief instructions on how to run the tool.
If you are interested in building Prediction and Classification models in Excel using Feedforward-Backpropagation Neural Network, here are two small Excel based tools for you. Also, if you are interested in Tree based Classification models, here is a Tree based classifier in Excel.
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Size: 214016 |
Author:Jessie |
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Description: 人工神经网络SOM，通过输入训练样本，训练次数等参数进行训练，实现模式分类-Artificial neural network SOM, by entering the training sample, training times and other parameters of training to achieve pattern classification Platform: |
Size: 267264 |
Author:苏米 |
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Description: 神经网络源码，可应用于遥感图像的分类，采用的包括bp、kohonen。可以作为范例来学习。-Neural network source code can be used in remote sensing image classification, using the included bp, kohonen. Can serve as examples to learn. Platform: |
Size: 5120 |
Author:葛宏 |
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Description: 该代码为基于Kohonen网络的分类算法，注意归一化处理以及网络的构建-The code for the classification algorithm based on Kohonen networks, pay attention to the normalization processing and network building Platform: |
Size: 2048 |
Author:南方 |
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Description: 一个神经网络计算的库，实现几个通用神经网络体系和训练方法，自识别图，弹性网络等.like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning.-In this article, a C# library for neural network computations is described. The library implements several popular neural network architectures and their training algorithms, like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning. The usage of the library is demonstrated on several samples:
• Classification (one-layer neural network trained with perceptron learning algorithms)
• Approximation (multi-layer neural network trained with back propagation learning algorithm)
• Time Series Prediction (multi-layer neural network trained with back propagation learning algorithm)
• Color Clusterization (Kohonen Self-Organizing Map)
• Traveling Salesman Problem (Elastic Network). Platform: |
Size: 242688 |
Author:calford |
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Description: SOM神经网络，Self-Organizing Map 的缩写，即自组织映射。
1981年芬兰Helsink大学的T.Kohonen教授提出一种自组织特征映射网，简称SOM网，又称Kohonen网。 Kohonen认为：一个神经网络接受外界输入模式时，将会分为不同的对应区域，各区域对输入模式具有不同的响应特征，而且这个过程是自动完成的。自组织特征映射正是根据这一看法提出来的，其特点与人脑的自组织特性相类似。
由于它的强大功能，多年来，神经网络在数据分类、知识获取、过程监测、故障识别等领域中得到广泛运用。-SOM neural network, Self-Organizing Map acronym, that self-organizing map. 1981 University Professor T.Kohonen Finland Helsink propose a self-organizing feature map network, referred to as SOM network, also known as Kohonen network. Kohonen think: when a neural network to outside input mode, will be divided into different corresponding regions, the regional input to the model with different response characteristics, and this process is done automatically. It is similar to the self-organizing map according to this view put forward, which is characterized by self-organization and phase characteristics of the human brain. Because of its powerful over the years, neural networks in data classification, knowledge acquisition, process monitoring, fault identification and other areas to be widely used. Platform: |
Size: 1024 |
Author: |
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Description: SOM神经网络：Self-Organizing Map 的缩写，即自组织映射。
1981年芬兰Helsink大学的T.Kohonen教授提出一种自组织特征映射网，简称SOM网，又称Kohonen网。 Kohonen认为：一个神经网络接受外界输入模式时，将会分为不同的对应区域，各区域对输入模式具有不同的响应特征，而且这个过程是自动完成的。自组织特征映射正是根据这一看法提出来的，其特点与人脑的自组织特性相类似。
由于它的强大功能，多年来，神经网络在数据分类、知识获取、过程监测、故障识别等领域中得到广泛运用。-SOM Neural Networks: Self-Organizing Map acronym, that self-organizing map. 1981 University Professor T.Kohonen Finland Helsink propose a self-organizing feature map network, referred to as SOM network, also known as Kohonen network. Kohonen think: when a neural network to outside input mode, will be divided into different corresponding regions, the regional input to the model with different response characteristics, and this process is done automatically. It is similar to the self-organizing map according to this view put forward, which is characterized by self-organization and phase characteristics of the human brain. Because of its powerful over the years, neural networks in data classification, knowledge acquisition, process monitoring, fault identification and other areas to be widely used. Platform: |
Size: 1024 |
Author: |
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Description: 自组织网络(Kohonen)特点：1)适用于超大样本的无监督分类；2)其结果常常需要与统计分析一起使用来解释分类结果；3)能够识别新类型，但功能较差。
-1) is suitable for large sample of unsupervised classification 2) the results often need to use with statistical analysis to explain the classification results 3) to identify new types, but poor function. Platform: |
Size: 46080 |
Author:三苏 |
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Description: 该代码为基于有导师监督的Kohonen网络的分类算法-The code is based on the supervision of the instructors Kohonen network classification algorithm Platform: |
Size: 2048 |
Author:liujunrong |
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Search - kohonen classification