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A simplified linguistic information feedback-based dynamical fuzzy system

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Abstract

Inspired by the linguistic information feedback-based dynamical fuzzy system (LIFDFS) recently proposed by the authors, we present a simplified LIFDFS (S-LIFDFS) model in this paper, which has a simpler linguistic information feedback structure. Compared with the LIFDFS, the S-LIFDFS can offer us with a considerably reduced computational complexity. We first give a detailed description of its underlying principle. Based on the gradient descent method, an adaptive learning algorithm for the feedback parameters is next derived. We also discuss the application of this S-LIFDFS in time series prediction. Three evaluation examples including prediction of two artificial time sequences and the well-known Box–Jenkins gas furnace data are demonstrated here. Simulation results illustrate that with a compact structure, our S-LIFDFS can still retain the advantage of inherent dynamics of linguistic information feedback and is, therefore, well suited for handling temporal problems like prediction, modeling, and control.

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References

  1. Pedrycz W (1990) Fuzzy sets in pattern recognition: methodology and methods. Pattern Recogn 23(1):121–146

    Article  MathSciNet  Google Scholar 

  2. Lee CC (1900) Fuzzy logic in control systems: fuzzy logic controller, Parts I and II. IEEE Trans Syst Man Cybern 20(2):404–435

    Article  Google Scholar 

  3. Kwong WA, Passino KM, Laukonen EG, Yurkovich S (1995) Expert supervision of fuzzy learning systems for fault tolerant aircraft control. Proc IEEE 83(3):466–483

    Article  Google Scholar 

  4. 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

    Google Scholar 

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

    Article  Google Scholar 

  6. Gao XZ, Ovaska SJ, Wang X (2009) A Linguistic Information feed-back-based dynamical fuzzy system (LIFBDFS) with learning algorithm. Neural Comput Appl 18(4):321–329

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Wang L-X (1997) A course in fuzzy systems and control. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  9. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  10. Lee C-H, Teng C-C (2000) Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans Fuzzy Syst 8(4):349–366

    Article  Google Scholar 

  11. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control, 2nd edn. Holden-Day, San Francisco

    MATH  Google Scholar 

  12. Farag WA, Quintana VH, Lambert-Torres G (1998) A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems. IEEE Trans Neural Netw 9(5):756–767

    Article  Google Scholar 

Download references

Acknowledgments

This research work was funded by the Academy of Finland under Grants 201353, 214144, and 135225. The authors would like to thank the anonymous reviewers for their insightful comments and constructive suggestions that have improved the paper.

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Authors and Affiliations

  1. Department of Electrical Engineering, Helsinki University of Technology, Otakaari 5 A, 02150, Espoo, Finland

    X. Z. Gao, S. J. Ovaska & X. Wang

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  1. X. Z. Gao
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  2. S. J. Ovaska
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  3. X. Wang
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Correspondence to X. Z. Gao.

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Gao, X.Z., Ovaska, S.J. & Wang, X. A simplified linguistic information feedback-based dynamical fuzzy system. Neural Comput & Applic 19, 1029–1041 (2010). https://doi.org/10.1007/s00521-010-0354-z

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  • DOI: https://doi.org/10.1007/s00521-010-0354-z

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