Online Learning of non-stationary data streams is a challenging task. This work presents an online training method for a Single hidden Layer Feedforward neural Network (SLFN) that learns sample-by-sample, using an adjustable sliding window to adapt the network when data has changed. The method presents a fast training procedure, estimating hidden and output layer parameters independently. Tests with four synthetic datasets showed a good accuracy and quick recovery after drift occurrences. The proposed method is also applied to a real dataset from an industrial process in order to address the anomaly detection task, with the network acting as a classifier. Results show that the method is able to detect drifts prior to anomalies in the pre-fault periods, in the real situation that appeared in the industrial dataset.

在线学习非平稳数据流是一项艰巨的任务。这项工作提出了一种用于单隐藏层前馈神经网络（SLFN）的在线培训方法，该方法可以按样本学习样本，并使用可调整的滑动窗口在数据发生变化时适应网络。该方法提出了一种快速的训练程序，可以独立地估计隐藏层参数和输出层参数。使用四个合成数据集进行的测试显示出良好的准确性，并在发生漂移后快速恢复。所提出的方法也被应用于来自工业过程的真实数据集，以解决异常检测任务，其中网络充当分类器。结果表明，该方法能够在工业数据集中出现的实际情况下，在故障前的异常之前检测漂移。