Self organizing neural networks with a split/merge algorithm

Arun D. Kulkarni, G. Whitson
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引用次数: 3

Abstract

In this paper we present a new learning algorithm for Artificial Neural Networks (ANN) using a split/merge technique. An ANN model with the new algorithm has been developed and tested on a PC. The model detects the similarity between the input patterns, and identifies the number of categories present in the input samples. The algorithm is similar to a competitive learning algorithm. Unlike the competitive learning algorithm, in this algorithm we use two types of weights: long term weights (LTWs) and short term weights (STWs). The network stability is provided by the LTWs, whereas the network plasticity is provided by STWs. As an illustration, the model is used to categorize pixels in a multispectral image. The categorization is based on the observed spectral signature at each pixel.
带有分裂/合并算法的自组织神经网络
本文提出了一种基于分割/合并的人工神经网络学习算法。利用新算法建立了一个人工神经网络模型,并在PC机上进行了测试。该模型检测输入模式之间的相似性,并识别输入样本中存在的类别数量。该算法类似于竞争性学习算法。与竞争性学习算法不同,在该算法中,我们使用两种类型的权重:长期权重(ltw)和短期权重(stw)。网络的稳定性由ltw提供,而网络的可塑性由stw提供。作为示例,该模型用于多光谱图像中的像素分类。分类是基于在每个像素上观测到的光谱特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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