The Approximate Individual Sample Learning Entropy is based on incremental learning of a predictor , where x(k) is an input vector of a given size at time k, w is a vector of weights (adaptive parameters), and h is a prediction horizon. The basic assumption is that, after the underlying process x changes its behavior, the incrementally learning system will adapt the weights w to improve the predictor . Our goal is to detect a change in the behavior of the weight increment process. The main idea of this paper is based on the fact that weight increments △w(k), where △w(k) = w(k + 1) − w(k), create a weakly stationary process until a change occurs. Once a novelty behavior of the underlying process x(k) occurs, the process △w(k) changes its characteristics (eg, the mean or variation). We suggest using convenient characteristics of △w(k) in a multivariate detection scheme (eg, the Hotelling's T² control chart).

中文翻译：

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基于学习熵的新颖性检测

近似个体样本学习熵基于预测变量的增量学习，其中x（k）是在时间k处给定大小的输入向量，w是权重（自适应参数）的向量，h是预测范围。基本假设是，在基础过程x改变其行为之后，增量学习系统将调整权重w以改善预测变量。我们的目标是检测体重增加过程的行为变化。本文的主要思想是基于这样的事实：权重增加△ w（k），其中△ w（k）=  w（k  +1）  -w（k），创建一个弱固定过程，直到发生变化。一旦基础过程x（k）出现新奇行为，过程△ w（k）就会改变其特征（例如，均值或变异）。我们建议在多变量检测方案（例如，Hotelling的T ²控制图中）中使用△ w（k）的便利特性。