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6.12. References
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- 2. R. Dave, Characterization and detection of noise in clustering, Pattern Recognition Letters, 12, 1992, 657 - 664.
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- 5. H. Ishibuchi, R. Fujioka, H. Tanaka, An architecture of neural networks for input vectors of fuzzy numbers. Proc. IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE 92), San Diego, March 8-12, pp. 1293-1300, 1992.
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- 7. R. Krishnapuram, J. M. Keller, A possibilistic approach to clustering, IEEE Trans. on Fuzzy Systems, 1, 1993, 98-110.
- 8. W. Pedrycz, Selected issues of frame of knowledge representation realized by means of linguistic labels, Int. J. of Intelligent Systems, 7, 1992, 155-170.
- 9. W. Pedrycz, Fuzzy Sets Engineering, CRC, Boca Raton, FL, 1995.
- 10. W. Pedrycz, Fuzzy multimodels, IEEE Trans. on Fuzzy Systems, 4, 1996, 139 - 148.
- 11. W. Pedrycz, Conditional Fuzzy C-Means, Pattern Recognition Letters, 17, 1996, 625 - 632.
- 12. W. Pedrycz, A. F. Rocha, Fuzzy-set based models of neurons and knowledge-based networks, IEEE Trans. on Fuzzy Systems, 1, 254-266, 1993.
- 13. D.E. Rumelhart, J.L. McLelland, Parallel Distributed Processing, MIT Press, Cambridge, MA, 1986.
- 14. F.M. Silva, L.B. Almeida, Acceleration Techniques for the Back-Propagation Algorithm, Lecture Notes in Computer Science, Springer-Verlag, Vol. 412, pp. 110-119, 1990.
- 15. T. Yamakawa, S. Tomoda, A fuzzy neuron and its application to pattern recognition. Proc. Third IFSA Congress, Seattle, August 6-11, 30-38, 1989.
- 16. L.A. Zadeh, Fuzzy sets and information granularity, In: M.M. Gupta, R.K. Ragade, R.R. Yager, eds., Advances in Fuzzy Set Theory and Applications, North Holland, Amsterdam, 3-18, 1979.
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