Skip to main content
SpringerLink
Log in

A linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) with learning algorithm

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this study, the linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) proposed earlier by the authors is first introduced. The principles of α-level sets and backpropagation through time approach are also briefly discussed. We next employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFBDFS. With this training algorithm, our LIFBDFS indeed becomes a potential candidate in solving real-time modeling and prediction problems.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Kosko B (1997) Fuzzy engineering. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  2. Zhang J, Morris AJ (1999) Recurrent neuro-fuzzy networks for nonlinear process modeling. IEEE Trans Neural Netw 10(2):313–326

    Article  Google Scholar 

  3. Zhang J (2005) Modeling and optimal control of batch processes using recurrent neuro-fuzzy networks. IEEE Trans Fuzzy Syst 13(4):417–427

    Article  Google Scholar 

  4. Vlcek Z (2004) Analysis of autoregressive fuzzy systems. In: Proceedings of the IEEE international conference on fuzzy systems, Budapest, Hungary, pp 1233–1238

  5. Liu H, Wang X, Zhang W, Xu G (2002) Market clearing price forecasting based on dynamic fuzzy system. In: Proceedings of the international conference on power system technology, Kunming, China, pp 890–896

  6. Gao XZ, Ovaska SJ (2003) Dynamical fuzzy systems with linguistic information feedback. In: Melo-Pinto P, Teodorescu H-N, Fukuda T (eds) Systematic organization of information in fuzzy systems. IOS Press, Amsterdam, The Netherlands, pp 179–195

    Google Scholar 

  7. Gao XZ, Ovaska SJ, Wang X (2007) Linguistic information in dynamical fuzzy systems. In: 12th online world conference on soft computing in industrial applications

  8. Gao XZ, Ovaska SJ, Wang X (2005) A simplified linguistic information feedback-based dynamical fuzzy system with learning algorithm—theory and evaluation. In: Proceedings of IEEE mid-summer workshop on soft computing in industrial applications, Espoo, Finland, pp 44–56

  9. Gao XZ, Ovaska SJ (2006) Linguistic information feed-forward-based dynamical fuzzy systems. IEEE Trans Syst Man Cybern Part C 36(4):453–463

    Article  Google Scholar 

  10. Martikainen J, Ovaska SJ (2005) Optimizing dynamical fuzzy systems using aging evolution strategies. In: Proceedings of the 9th IASTED international conference on artificial intelligence and soft computing, Benidorm, Spain, pp 5–10

  11. Werbos PJ (1990) Backpropagation through time: What it does and how to do it. Proc IEEE 78(10):1550–1560

    Article  Google Scholar 

  12. Ahmad AM, Ismail S, Samaon DF (2004) Recurrent neural network with backpropagation through time for speech recognition. In: Proceedings of the IEEE international symposium on communications and information technology, Sapporo, Japan, pp 98–102

  13. Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Upper Saddle River

    Google Scholar 

  14. Tang KS, Man KF, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Process 13(6):22–37

    Article  Google Scholar 

  15. Lin C-T, Lu Y-C (1995) A neural fuzzy system with linguistic teaching signals. IEEE Trans Fuzzy Syst 3(2):169–188

    Article  Google Scholar 

  16. Uehara K, Fujise M (1993) Fuzzy inference based on families of α-level sets. IEEE Trans Fuzzy Syst 1(2):111–124

    Article  Google Scholar 

  17. De Jesús O, Hagan MT (2007) Backpropagation algorithms for a broad class of dynamic networks. IEEE Trans Neural Netw 18(1):14–27

    Article  Google Scholar 

  18. Pearlmutter BA (1995) Gradient calculations for dynamic recurrent neural networks: a survey. IEEE Trans Neural Netw 6(5):1212–1228

    Article  Google Scholar 

Download references

Acknowledgments

This research work was funded by the Academy of Finland under Grant 201353. The authors would like to thank the anonymous reviewers for their insightful comments and constructive suggestions that have improved the paper.

Author information

Authors and Affiliations

  1. Institute of Intelligent Power Electronics, Helsinki University of Technology, Otakaari 5 A, 02150, Espoo, Finland

    X. Z. Gao, S. J. Ovaska & X. Wang

Authors
  1. X. Z. Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. J. Ovaska
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. X. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to X. Z. Gao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gao, X.Z., Ovaska, S.J. & Wang, X. A linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) with learning algorithm. Neural Comput & Applic 18, 321–329 (2009). https://doi.org/10.1007/s00521-008-0183-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-008-0183-5

Keywords

Search

Navigation