早期发现异常紧急行为

L. Spaanenburg
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引用次数: 0

摘要

突发行为已经成为基于通信网络的自动化系统的一大困扰。对网络的集中监控通常来得太晚,无法抑制不必要的行为。它需要以分散的方式标记国家变化的趋势。本文通过对前馈网络模型学习行为的考察,讨论了局部意识的作用。随着时间的推移,权重变化的相关运动清楚地表明了这种深刻的变化,正如工业自动化的一些初步经验所证明的那样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early detection of abnormal emergent behaviour
Emergent behaviour has become a plague of automation systems based on communication networks. Centralized monitoring of the network comes generally to late to suppress unwanted behaviour. It is required to mark the tendency towards state changes in a decentralized manner. The paper discusses the role of local awareness by inspection of the model learning behaviour of feed-forward networks. The correlated movement of weight changes over time provides a clear indication of such profound changes, as demonstrated by some initial experience in industrial automation.
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