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Applications of artificial neural network based fuzzy inference system

  • Ernest Czogała
  • Jacek Łęski
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 47)

Abstract

In previous chapters we introduced the artificial neural network based fuzzy inference system (ANNBFIS) network structure. The learning methods, clustering of input space, use of different fuzzy implications in inference process and other related topics are shown. In this chapter we will show several applications of ANNBFIS to solving many practical problems, as: time series prediction, signal compression, classifications of patterns, system identifications, control and equalization of digital communication channel. All above applications will be tested on benchmark data sets. These data can be easily obtained via Internet. This approach ensures easy comparison of the proposed system to systems known from literature, and the readers can compare their own systems to the system presented in this book.

Keywords

Artificial Neural Network Fuzzy Inference System Cluster Validity Chaotic Time Series Average Data Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Bibliographical notes

  1. Schuster, H.G. (1988): Deterministic chaos. 2nd edn. VCH Verlagsgesellschaft, New YorkGoogle Scholar
  2. Fisher, R., Akay, M. (1998): Fractal analysis of heart rate variability. In: Akay, M. (ed.): Time frequency and wavelets in biomedical signal processing IEEE Press, New YorkGoogle Scholar
  3. Jang, J.-S.R. (1993a): ANFIS: adaptive-network-based fuzzy inference system IEEE Trans.Systems, Man and Cybernetics 23 (3), 665–685CrossRefGoogle Scholar
  4. Cho, K.B., Wang, B.H. (1996): Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction. Fuzzy Sets and Systems 83, 325–339MathSciNetCrossRefGoogle Scholar
  5. Wang, L.-X. (1994): Adaptive fuzzy systems and control. Prentice-Hall, New YorkGoogle Scholar
  6. Cohen, A. (1986): Biomedical signal processing, Vol. I: Time and frequency domains analysis, Vol. I I: Compression and automatic recognition. CRC Press, Boca RatonGoogle Scholar
  7. Hamilton, P.S., Tompkins, W.C. (1991): Compression of the ambulatory ECG by average beat subtraction and residual differencing. IEEE Trans. Biomed. Eng. 38, 253–259CrossRefGoogle Scholar
  8. Duda, R.O., Hart, P.E. (1973): Pattern classification and scene analysis. John Wiley & Sons, New YorkMATHGoogle Scholar
  9. Nie, J., Linkens, D.A. (1993): Learning control using fuzzified self-organizing radial basis function network. IEEE Trans. Fuzzy Systems 1 (4), 280–287CrossRefGoogle Scholar
  10. Cordòn, O., Herrera, F. (1997): Identification of linguistic fuzzy models by means of genetic algorithms. In: Hellendoorn, H., Driankov, D. (eds.): Fuzzy model identification. Selected approaches, Springer. New YorkGoogle Scholar
  11. Tou, J.T., Gonzalez, R.C. (1974): Pattern recognition principles. Adison-Wesley, LondonMATHGoogle Scholar
  12. Fukunaga, K. (1990): Introduction to statistical pattern recognition. 2nd edn. Academic Press, San DiegoMATHGoogle Scholar
  13. Ripley, B.D. (1996): Pattern recognition and neural network. Cambridge University Press, Cambridge New York MelbourneGoogle Scholar
  14. Devroye, L., Györfi, L., Lugosi, G. (1996): A probabilistic theory of pattern recognition. Springer, New YorkMATHGoogle Scholar
  15. Mitra, S., Pal, S.K. (1996): Fuzzy self-organization, inferencing and rule generation. IEEE Trans. System, Man and Cybernetics 26 (5), 608–619CrossRefGoogle Scholar
  16. Box, G.E.P., Jenkins, G.M. (1976): Time series analysis. Forecasting and control. Holden-Day, San FranciscoGoogle Scholar
  17. Eykhoff, P. (1974): System identification. Parameter and state estimation. John Wiley & Sons, LondonGoogle Scholar
  18. Söderström, T., Stoica, P. (1994): System identification. Prentice-Hall, New YorkGoogle Scholar
  19. Lindskog, P. (1997): Fuzzy identification from a gray box modeling point of view. In: Hellendoorn, H., Driankov, D. (eds.): Fuzzy model identification. Selected approaches. Springer, New YorkGoogle Scholar
  20. Jang, J.-S.R., Sun, C.-T., Mizutani, E. (1997): Neuro-fuzzy and soft computing. A computational approach to learning and machine intelligence. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  21. Wang, L.-X. (1994): Adaptive fuzzy systems and control. Prentice-Hall, New YorkGoogle Scholar
  22. Wang, L.-X. (1998): A course in fuzzy systems and control. Prentice-Hall, New YorkGoogle Scholar
  23. Haykin, S, Thomson, D.J. (1998): Signal detection in a nonstationary environment reformulated as an adaptive pattern classification problem. Proceedings IEEE 86 (11), 2325–2344CrossRefGoogle Scholar
  24. Jang, J.-S.R. (1992): Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans. Neural Networks 3 (5), 714–723CrossRefGoogle Scholar
  25. Kim, H.M., Kosko, B. (1996): Fuzzy prediction and filtering in impulsive noise. Fuzzy Sets and Systems 77, 15–33CrossRefGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2000

Authors and Affiliations

  • Ernest Czogała
    • 1
  • Jacek Łęski
    • 1
  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland

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