Detecting outliers in complex nonlinear systems controlled by predictive control strategy

Biao Wang, Zhizhong Mao and Keke Huang

Chaos, Solitons & Fractals, 2017, vol. 103, issue C, 588-595

Abstract: 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.

Keywords: Outlier detection; Complex nonlinear system; Model predictive control; Ensemble learning; One-class classification (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:103:y:2017:i:c:p:588-595

DOI: 10.1016/j.chaos.2017.07.018

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