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Adaptive Localizer Based on Splitting Trees

机译:基于分裂树的自适应定位器

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摘要

When testing a black box system that cannot be reset, it may be useful to use a localizer procedure that will ensure that the test sequence goes at some point through a state that can be identified with a characterizing set of input sequences. In this paper, we propose a procedure that will localize when the separating sequences are organized in a splitting tree. Compared to previous localizing sequences based on characterization sets, using the tree structure one can define an adaptive localizer, and the complexity of localizing depends on the height of the tree instead of the number of states.
机译:在测试无法复位的黑匣子系统时,使用本地化器过程可能是有用的,该过程可以确保测试序列通过可以用特征集合集合设置的状态识别的状态。在本文中,我们提出了一种程序,当分离树上组织分离序列时将定位。与基于表征集的先前定位序列相比,使用树结构可以定义自适应定向器,并且本地化的复杂性取决于树的高度而不是状态的数量。

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