Detecting outliers in complex nonlinear systems controlled by predictive control strategy

Detecting outliers in complicated nonlinear systems that are controlled by model predictive control is a significant work for engineering applications. Based on the features of data in practical systems, we propose a one-class classification ensemble method incorporating the notion of Feature Subspace with Bagging. Clustering and PCA (Principal Component Analysis) are integrated to obtain a more informative feature space, where Feature subspaces and bootstrap replications are implemented orderly to generate more accuracy and diverse base learners. A detector is constructed based on the above methodology, and a model updating strategy is also provided. By means of comparison with competitive methods, the effectiveness of the proposed detector has been verified.