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Detecting outliers in complex nonlinear systems controlled by predictive control strategy

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  • Wang, Biao
  • Mao, Zhizhong
  • Huang, Keke

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.

Suggested Citation

  • Wang, Biao & Mao, Zhizhong & Huang, Keke, 2017. "Detecting outliers in complex nonlinear systems controlled by predictive control strategy," Chaos, Solitons & Fractals, Elsevier, vol. 103(C), pages 588-595.
    • Handle: RePEc:eee:chsofr:v:103:y:2017:i:c:p:588-595
    DOI: 10.1016/j.chaos.2017.07.018
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    References listed on IDEAS

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    1. Chen, Hsien-Keng, 2005. "Global chaos synchronization of new chaotic systems via nonlinear control," Chaos, Solitons & Fractals, Elsevier, vol. 23(4), pages 1245-1251.
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    Cited by:

    1. Wang, Biao & Mao, Zhizhong & Huang, Keke, 2019. "Detecting outliers for complex nonlinear systems with dynamic ensemble learning," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 98-107.

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