Online learning of neural networks using random projections and sliding window: A case study of a real industrial process

摘要

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.