Skip to main content

Advertisement

Springer Nature Link
Log in

Performance analysis of fuzzy BLS using different cluster methods for classification

  • Letter
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Rong H J, Huang G B, Sundararajan N, et al. Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern B, 2009, 39: 1067–1072

    Article  Google Scholar 

  2. Wong S Y, Yap K S, Yap H J, et al. On equivalence of FIS and ELM for interpretable rule-based knowledge representation. IEEE Trans Neural Netw Learn Syst, 2015, 26: 1417–1430

    Article  MathSciNet  Google Scholar 

  3. Ouyang C S, Kao T C, Cheng Y Y, et al. An improved fuzzy extreme learning machine for classification and regression. In: Proceedings of International Conference on Cybernetics, Robotics and Control (CRC), Hong Kong, 2016. 91–94

    Google Scholar 

  4. Feng S, Chen C L P. A fuzzy restricted Boltzmann machine: novel learning algorithms based on the crisp possibilistic mean value of fuzzy numbers. IEEE Trans Fuzzy Syst, 2018, 26: 117–130

    Article  Google Scholar 

  5. Segatori A, Bechini A, Ducange P, et al. A distributed fuzzy associative classifier for big data. IEEE Trans Cybern, 2018, 48: 2656–2669

    Article  Google Scholar 

  6. Feng S, Chen C L P. Fuzzy broad learning system: a novel neuro-fuzzy model for regression and classification. IEEE Trans Cybern, 2020, 50: 414–424

    Article  Google Scholar 

  7. Chen C L P, Liu Z L. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans Neural Netw Learn Syst, 2018, 29: 10–24

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61751202, 61751205, 61572540), Macau Science and Technology Development Fund (Grant Nos. 019/2015/A1, 079/2017/A2, 024/2015/AMJ), Multiyear Research Grants of University of Macau, and Teacher Research Capacity Promotion Program of Beijing Normal University, Zhuhai.

Author information

Authors and Affiliations

  1. School of Applied Mathematics, Beijing Normal University, Zhuhai, 519087, China

    Shuang Feng

  2. Faculty of Science and Technology, University of Macau, Macau, 999078, China

    Shuang Feng & C. L. Philip Chen

Authors
  1. Shuang Feng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. C. L. Philip Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to C. L. Philip Chen.

Additional information

Supporting information

Appendix A. The supporting information is available online at info.scichina.com and link. springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, S., Chen, C.L.P. Performance analysis of fuzzy BLS using different cluster methods for classification. Sci. China Inf. Sci. 64, 149205 (2021). https://doi.org/10.1007/s11432-018-9630-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-018-9630-0