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A new method for system modelling and pattern classification

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Języki publikacji
EN
Abstrakty
EN
In this paper we present a new class of neuro-fuzzy systems designed for system modelling and pattern classification. Our approach is characterized by automatic determination of fuzzy inference in the process of learning. Moreover, we introduce several flexibility concepts in the design of neuro-fuzzy systems. The method presented in the paper is characterized by high accuracy which outperforms previous techniques applied for system modelling and pattern classification.
Rocznik
Strony
11--24
Opis fizyczny
Bibliogr. 40 poz., 15 rys., 8 tab.
Twórcy
autor
  • Technical University of Częstochowa, Department of Computer Engineering, 36 Armii Krajowej Ave., 42-200 Częstochowa, Poland
Bibliografia
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  • [2] K. Hirota, Industrial Applications of Fuzzy Technology, Berlin: Springer Verlag, 1993.
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  • [5] J. Kacprzyk, Multistage Fuzzy Control, Chichester: John Wiley & Sons, 1997.
  • [6] L. Rutkowski, Flexible Neuro-Fuzzy Systems, Dordrecht: Kluwer Academic Publishers, 2004.
  • [7] R. L. Eubank, Nonparametric Regression and Spline Smoothing, New York: Marcel Dekker, 1999.
  • [8] L. Rutkowski, New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing, Berli n: Springer-Verlag, 2004.
  • [9] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Berlin: Springer-Verlag, 1992.
  • [10] Z. Pawlak, “Rough sets”, Intern. Journal of Information and Computer Science 11, 341 (1982).
  • [11] Z. Pawlak, Rough Sets. Theoretical Aspects of Reasoning About Data, Dordrecht: Kluwer Academic Publishers, 1991.
  • [12] Z. Bubnicki, “Uncertain variables and their application to decision making”, IEEE Trans. on SMC, Part A: Systems and Humans 31, 587–596 (2001).
  • [13] Z. Bubnicki, Uncertain Logics, Variables and Systems, Berlin: Springer-Verlag, 2002. [14] R. A. Aliev and R. R. Aliev, Soft Computing and its Applications, Singapore: World Scientific Publishing, 2001.
  • [15] V. Kecman, Learning and Soft Computing, MIT, Cambri dge 2001.
  • [16] M. Y. Chen and D. A. Linkens, “A systematic neurofuzzy modeling framework with application to material property prediction”, IEEE Trans. on Fuzzy Systems 31, 781–790 (2001).
  • [17] Gonz´alez and R. P´erez, “SLAVE: a genetic learning system based on an iterative approach”, IEEE Trans. on Fuzzy Systems 7, 176–191 (1999).
  • [18] J. S. Jang, C. T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing, London: Prentice Hall, 1997.
  • [19] N. Kasabov, “DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction”, IEEE Trans. on Fuzzy Systems 10, 144–154 (2002).
  • [20] E. Kim, M. Park, S. Ji and M. Park, “A new approach to fuzzy modeling”, IEEE Trans. on Fuzzy Systems 5, 328–337 (1997).
  • [21] H. Roubos H. and M. Setnes, “Compact and transparent fuzzy models and classifiers through iterative complexity reduction”, IEEE Trans. on Fuzzy Systems 9, 516–524 (2001).
  • [22] L. Rutkowskiand K. Cpałka, “Flexible structures of neurofuzzy systems”, Quo Vadis Computational Intelligence, Studies in Fuzziness and Soft Computing, Berlin: Springer-Verlag, Vol. 54, 479–484 (2000).
  • [23] L. Rutkowskiand K. Cpałka, “A general approach to neuro-fuzzy systems”, The 10th IEEE Intern. Conference on Fuzzy Systems, Melbourne, 2001.
  • [24] L. Rutkowski and K. Cpałka, “Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems”, Technical Report, Department of Computer Engineering, Technical University of Czestochowa, Częstochowa 2002.
  • [25] L. Rutkowskiand K. Cpałka, “A neuro-fuzzy controller with a compromise fuzzy reasoning”, Control and Cybernetics 31(2), 297–308 (2002).
  • [26] L. Rutkowski and K. Cpałka, “Flexible weighted neuro-fuzzy systems”, 9th Intern. Conference on Neural Information Processing (ICONIP’02), Orchid Country Club, Singapore, 2002.
  • [27] L. Rutkowskiand K. Cpałka, “Flexible neuro-fuzzy systems”, IEEE Trans. Neural Networks 14, 554–574 (2003).
  • [28] M. Setnes and H. Roubos, “GA-fuzzy modeling and classification complexity and performance”, IEEE Trans. on Fuzzy Systems 8, 509–521 (2000).
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  • [35] M. Dong and R. Kothari, “Look-ahead based fuzzy decision tree induction”, IEEE Trans. on Fuzzy Systems 9, 461–468 (2001).
  • [36] L. Rutkowskiand E. Rafajłowicz, “On global rate of convergence of some nonparametric identification procedures”, IEEE Trans. on Automatic Control AC-34(10), 1089–1091 (1989).
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  • [38] L. Rutkowski, “Multiple Fourier series procedures for extraction of nonlinear regressions from noisy data”, IEEE Trans. on Signal Processing 41, 3062–3065 (1993).
  • [39] L. Rutkowski, “Adaptive probabilistic neural networks for pattern classification in time-varying environment”, IEEE Trans. on Neural Networks 15 (2004).
  • [40] L. Rutkowski, “Generalized regression neural networks in time-varying environment”, IEEE Trans. on Neural Networks 15, (2004).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG5-0001-0010
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