A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies
A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies
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DOI：
10.1016/j.chemolab.2012.07.005
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发表时间：
2012-08-15
影响因子：
3.9
通讯作者：
Vasighi, Mandi
中科院分区：
文献类型：
--
作者：
Ballabio, Davide;Vasighi, Mandi
Kohonen映射和反向传播神经网络是基于人工神经网络的两种最流行的学习策略。Kohonen映射(或自组织映射)基本上是自组织系统，能够解决无监督而不是监督的问题，而反向传播人工神经网络非常类似于Kohonen映射，但为了处理监督建模，在Kohonen层增加了输出层。最近，反向传播人工神经网络的改进允许引入新的有监督神经网络策略，如有监督Kohonen网络和XY融合网络。这是一个用于计算Kohonen映射和用于监督分类的派生方法的模块集合，例如反向传播人工神经网络、监督Kohonen网络和XY融合网络。工具箱包括一个图形用户界面(GUI)，它允许在易于使用的图形环境中进行计算。它的目标是对初学者和高级用户都有用。这里讨论了工具箱的使用，并给出了一个适当的实例。(C)2012爱思唯尔B.V.保留所有权利。
Kohonen maps and Counterpropagation Neural Networks are two of the most popular learning strategies based on Artificial Neural Networks. Kohonen Maps (or Self Organizing Maps) are basically self-organizing systems which are capable to solve the unsupervised rather than the supervised problems, while Counterpropagation Artificial Neural Networks are very similar to Kohonen maps, but an output layer is added to the Kohonen layer in order to handle supervised modelling. Recently, the modifications of Counterpropagation Artificial Neural Networks allowed introducing new supervised neural network strategies, such as Supervised Kohonen Networks and XY-fused Networks.In this paper, the Kohonen and CP-ANN toolbox for MATLAB is described. This is a collection of modules for calculating Kohonen maps and derived methods for supervised classification, such as Counterpropagation Artificial Neural Networks, Supervised Kohonen Networks and XY-fused Networks. The toolbox comprises a graphical user interface (GUI), which allows the calculation in an easy-to-use graphical environment. It aims to be useful for both beginners and advanced users of MATLAB. The use of the toolbox is discussed here with an appropriate practical example. (C) 2012 Elsevier B.V. All rights reserved.