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.
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Bibliographical notes
Schuster, H.G. (1988): Deterministic chaos. 2nd edn. VCH Verlagsgesellschaft, New York
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 York
Jang, J.-S.R. (1993a): ANFIS: adaptive-network-based fuzzy inference system IEEE Trans.Systems, Man and Cybernetics 23 (3), 665–685
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, 325–339
Wang, L.-X. (1994): Adaptive fuzzy systems and control. Prentice-Hall, New York
Cohen, A. (1986): Biomedical signal processing, Vol. I: Time and frequency domains analysis, Vol. I I: Compression and automatic recognition. CRC Press, Boca Raton
Hamilton, P.S., Tompkins, W.C. (1991): Compression of the ambulatory ECG by average beat subtraction and residual differencing. IEEE Trans. Biomed. Eng. 38, 253–259
Duda, R.O., Hart, P.E. (1973): Pattern classification and scene analysis. John Wiley & Sons, New York
Nie, J., Linkens, D.A. (1993): Learning control using fuzzified self-organizing radial basis function network. IEEE Trans. Fuzzy Systems 1 (4), 280–287
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 York
Tou, J.T., Gonzalez, R.C. (1974): Pattern recognition principles. Adison-Wesley, London
Fukunaga, K. (1990): Introduction to statistical pattern recognition. 2nd edn. Academic Press, San Diego
Ripley, B.D. (1996): Pattern recognition and neural network. Cambridge University Press, Cambridge New York Melbourne
Devroye, L., Györfi, L., Lugosi, G. (1996): A probabilistic theory of pattern recognition. Springer, New York
Mitra, S., Pal, S.K. (1996): Fuzzy self-organization, inferencing and rule generation. IEEE Trans. System, Man and Cybernetics 26 (5), 608–619
Box, G.E.P., Jenkins, G.M. (1976): Time series analysis. Forecasting and control. Holden-Day, San Francisco
Eykhoff, P. (1974): System identification. Parameter and state estimation. John Wiley & Sons, London
Söderström, T., Stoica, P. (1994): System identification. Prentice-Hall, New York
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 York
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 River
Wang, L.-X. (1994): Adaptive fuzzy systems and control. Prentice-Hall, New York
Wang, L.-X. (1998): A course in fuzzy systems and control. Prentice-Hall, New York
Haykin, S, Thomson, D.J. (1998): Signal detection in a nonstationary environment reformulated as an adaptive pattern classification problem. Proceedings IEEE 86 (11), 2325–2344
Jang, J.-S.R. (1992): Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans. Neural Networks 3 (5), 714–723
Kim, H.M., Kosko, B. (1996): Fuzzy prediction and filtering in impulsive noise. Fuzzy Sets and Systems 77, 15–33
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Czogała, E., Łęski, J. (2000). Applications of artificial neural network based fuzzy inference system. In: Fuzzy and Neuro-Fuzzy Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 47. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1853-6_7
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DOI: https://doi.org/10.1007/978-3-7908-1853-6_7
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-00389-3
Online ISBN: 978-3-7908-1853-6
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