动态环境中的学习:应用于演化系统的诊断

M. S. Mouchaweh, O. Ayad, N. Malki
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引用次数: 0

摘要

在动态环境中,数据特征可能随时间而漂移。随着时间的推移,这会导致增量学习算法的性能急剧恶化。这是因为使用的数据与新传入的数据的特征不一致。本文提出了一种动态环境下的学习方法。这种方法集成了一种机制,只使用最近和有用的模式来更新分类器,而不会出现“灾难性的遗忘”。该方法用于核电机组“原型快反应”蒸汽发生机组的声学泄漏检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning in dynamic environments: application to the diagnosis of evolving systems
In dynamic environments, data characteristics may drift over time. This leads to deteriorate dramatically the performance of incremental learning algorithms over time. This is because of the use of data which is no more consistent with the characteristics of new incoming one. In this paper, an approach for learning in dynamic environments is proposed. This approach integrates a mechanism to use only the recent and useful patterns to update the classifier without a “catastrophic forgetting”. This approach is used for the acoustic leak detection in the steam generator unit of the nuclear power generator “Prototype Fast React”.
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