Temporal sequence processing using recurrent SOM

T. Koskela, M. Varsta, J. Heikkonen, K. Kaski
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引用次数: 67

Abstract

Recurrent self-organizing map (RSOM) is studied in temporal sequence processing. RSOM includes a recurrent difference vector in each unit of the map, which allows storing temporal context from consecutive input vectors fed to the map. RSOM is a modification of the temporal Kohonen map (TKM). It is shown that RSOM learns a correct mapping from temporal sequences of a simple synthetic data, while TKM fails to learn this mapping. In addition, two case studies are presented, in which RSOM is applied to EEG based epileptic activity detection and to time series prediction with local models. Results suggest that RSOM can be efficiently used in temporal sequence processing.
使用循环SOM处理时间序列
研究了时间序列处理中的递归自组织映射(RSOM)。RSOM在地图的每个单元中包含一个循环差分向量,这允许存储从连续输入向量馈送到地图的时间上下文。RSOM是对时间Kohonen地图(TKM)的修改。结果表明,RSOM可以从简单合成数据的时间序列中学习到正确的映射,而TKM不能学习到这种映射。此外,本文还介绍了将RSOM应用于基于EEG的癫痫活动检测和基于局部模型的时间序列预测的两个案例。结果表明,RSOM可以有效地用于时间序列处理。
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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