Generic reconstruction technology based on RST for multivariate time series of complex process industries View Full Text


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Article Info

DATE

2012-05

AUTHORS

Ling-shuang Kong, Chun-hua Yang, Jian-qi Li, hong-qiu Zhu, Ya-lin Wang

ABSTRACT

In order to effectively analyse the multivariate time series data of complex process, a generic reconstruction technology based on reduction theory of rough sets was proposed. Firstly, the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology. Then, the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space, and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space. Finally, the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model. Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application. More... »

PAGES

1311-1316

References to SciGraph publications

  • 2009-10. Wavelet matrix transform for time-series similarity measurement in JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY
  • 2010-02. Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules in JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY
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    URI

    http://scigraph.springernature.com/pub.10.1007/s11771-012-1143-x

    DOI

    http://dx.doi.org/10.1007/s11771-012-1143-x

    DIMENSIONS

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