Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy

Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy

Автор: Y.F. Shi, L.H. He, J. Chen

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 1 vol.1, 2009 года.

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Based on fuzzy similarity degree, entropy, relative entropy and fuzzy entropy, the symmetric fuzzy relative entropy is presented, which not only has a full physical meaning, but also has succinct practicability. The symmetric fuzzy relative entropy can be used to measure the divergence between different fuzzy patterns. The example demonstrates that the symmetric fuzzy relative entropy is valid and reliable for fuzzy pattern recognition and classification, and its classification precision is very high.

Pattern recognition, fuzzy set, fuzzy similarity degree, relative entropy, symmetric fuzzy relative entropy, divergence

Короткий адрес: https://sciup.org/15010086

IDR: 15010086

Список литературы Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy

  • K.S. Fu and A.B Whinston, Pattern Recognition Theory and Application, Leyden, Netherlands, Noordhoff, 1977.
  • K.S. Fu, Syntactic Pattern Recognition with Applications. Prentice-Hall, Englewood Cliffs, NJ, 1982.
  • H.Bunke, Hybrid Methods in Pattern Recognition. In: Devijver, P., Kittler, J. Pattern Recognition Theory and Applications. Springer-Verlag, Berlin, Heidelberg, 1987, pp. 367-382.
  • P. Devijver, J. Kittler, Pattern Recognition: A Statistical Approach. Prentice Hall, 1982.
  • C.W. Therrien, Decision Estimation and Classification: An Introduction to Pattern Recognition and Related Topics. John Wiley & Sons, New York, 1989.
  • H. J. Zimmermann, Fuzzy Set Theory - and its Applications. Third edition, Boston, Dordrecht, London, 1996.
  • J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York, Plenum, 1981.
  • D. Gustafson and W. Kessel, Fuzzy Clustering with a Fuzzy Covariance Matrix. Proceedings of IEEE CDC, San Diego, California. IEEE Press, Piscataway, New Jersey, 1979, pp. 761-766.
  • I. Gath, and A.B. Geva, Unsupervised optimal Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1989), pp. 773-781.
  • R. Krishnapuram, and J. Keller, A Possibilistic Approach to Clustering. IEEE Transactions on Fuzzy Systems, 1(1993), pp. 98-110.
  • L.A. Zadeh, Fuzzy sets and their applications to classification and clustering. In: J. van Ryzin, Editor, Classification and Clustering, Academic Press, New York, pp. 251–299, 1977.
  • M. Cayrol, H., Farreny, and H. Prade, Possibility and Necessity in a Pattern-Matching Process. Proceedings of IXth International Congress on Cybernetics, Namur, Belgium, September, 1980, pp. 53-65.
  • M. Cayrol, H., Farreny, and H. Prade, Fuzzy Pattern Matching. Kybernetes, 11(1982), pp. 103-116.
  • D. Dubois, H. Prade, C. Testemale, Weighted Fuzzy Pattern Matching. Fuzzy Sets and Systems, 28(1988), pp. 313-331.
  • M. Grabisch, G. Bienvenu, J.F. Grandin, et al., A Formal Comparison of Probabilistic and Possibilistic Frameworks for Classification. Proceedings of 7th IFSA World Congress, Prague 1997, pp. 117-122.
  • N. Zahid, O. Abouelala, and M. Limouri, A.Unsupervised Fuzzy Clustering. Pattern Recognition Letters, 20( 1999), pp. 123-129.
  • T. Y. Young and K. S. Fu, Handbook of Pattern Recognition and Image proceeding, Academic Press, New York, 1986.
  • A. Samal and P. A. Iyenger, Automatic recognition and analysis of human faces and facial expressions: a survey, Pattern Recognition, 21(5)(1992), pp. 65-77.
  • J. P. Han, Robust telephone speech recognition based on channel compensation, Pattern Recognition, 32(5) (1999), pp. 1 061-1067.
  • M. Nakagawa, K. Ohnishi and H. Nakayasu, Humanoriented image recognition for industrial inspection system, 9th IEEE International Workshop Proceedings on Robot and Human Interactive Communication, pp. 52-56, 2000.
  • W. Woods, et al., The Use of Geometric and Gray-Level Models for Industrial Inspection, Pattern Recognition, 5(1)(1987), pp. 11-17.
  • Y.W. Li, S.Y. Chen and X.T. Nie, Fuzzy Pattern Recognition Approach to Construction Contractor Selection, Fuzzy Optimization and Decision Making, 4(2)(2005), pp.103-118.
  • C. J. Chao and F. P. Cheng, Fuzzy pattern recognition model for diagnosing cracks in RC structures, Journal of computing in civil engineering, 12(2)(1998), pp. 111-119.
  • J. Ozols and A. Borisov, Fuzzy classification based on pattern projections analysis, Pattern Recognition, 34(4)(2001), pp. 763-781.
  • J. C. Bezdek, J. Keller, R. Krisnapuram and N.R. Pal,Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Springer, 2005.
  • Y. Y. Mao, X. G. Zhang and L. S. Wang, Fuzzy pattern recognition method for assessing groundwater vulnerability to pollution in the Zhangji area, Journal of Zhejiang University, 7(11)(2006), pp. 1917-1922.
  • Y. W. Li, S. Y. Chen and X. T. Nie, Fuzzy pattern recognition approach to construction contractor selection, Fuzzy Optimization and Decision Making, 4(2)(2005), pp.103-118.
  • C. J. Rao, J. Pen and W. L. Chen, Novel method for fuzzy hybrid multiple attribute decision making, Advances in Soft Computing, Fuzzy Information and Engineering, 40(2007), pp. 583-591.
  • X.CH. Liu, Entropy, distance measure and similarity measure of fuzzy sets and their relations, Fuzzy Sets and Systems, 52(1992),pp.305-318
  • A. De Luca and S. Termini, A definition of a nonprobabilistic entropy in the setting of fuzzy set theory, Information and Control, 20(1972), pp. 305.
  • J. L. Fan, Y. L. Ma and W. X. Xie, On some properties of distance measures, Fuzzy Sets and Systems, 117 (2001), pp. 355-361.
  • Y. Kakihara, Abstract Methods in Information Theory, East South University Publisher, 1999.
  • H. Wan and K. Du, Fuzzy relative entropy and its application in fuzzy pattern recognition, Journal of Northwest University (Nature Science Edition), 36(2)(2006),pp.189-192.
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