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A fuzzy-filtered grey network technique for system state forecasting

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Abstract

A fuzzy-filtered grey network (FFGN) technique is proposed in this paper for time series forecasting and material fatigue prognosis. In the FFGN, the fuzzy-filtered reasoning mechanism is proposed to formulate fuzzy rules corresponding to different data characteristics; grey models are used to carry out short-term forecasting corresponding to different rules. A novel hybrid training method is proposed to adaptively update model parameters and improve training efficiency. The effectiveness of the developed FFGN is demonstrated by a series of simulation tests. It is also implemented for material fatigue prognosis. Test results show that the developed FFGN predictor can capture data characteristics effectively and forecast data trend accurately.

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Acknowledgments

This work was partly funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and eMech Systems Inc.

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Authors and Affiliations

  1. Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Dezhi Li & Fathy Ismail

  2. Department of Mechanical Engineering, Lakehead University, Thunder Bay, ON, P7B 5E1, Canada

    Wilson Wang

Authors
  1. Dezhi Li
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  2. Wilson Wang
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  3. Fathy Ismail
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Corresponding author

Correspondence to Wilson Wang.

Additional information

Communicated by E. Lughofer.

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Li, D., Wang, W. & Ismail, F. A fuzzy-filtered grey network technique for system state forecasting. Soft Comput 19, 3497–3505 (2015). https://doi.org/10.1007/s00500-014-1281-1

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