General fuzzy min-max neural network for clustering and classification

IEEE Transactions on Neural Networks

Volumn 11, Issue 3, 2000, Pages 769-783

  Gabrys, Bogdan ^a   Bargiela, Andrzej ^a  

^a NOTTINGHAM TRENT UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BOUNDARY CONDITIONS; FUZZY SETS; LEAK DETECTION; LEARNING SYSTEMS; PATTERN RECOGNITION; WATER DISTRIBUTION SYSTEMS;

CLUSTERING; FUZZY MIN-MAX NEURAL NETWORKS; UNSUPERVISED LEARNING;

NEURAL NETWORKS;

EID: 0034187078     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.846747     Document Type: Article

References (32)

1. S. Abe and R. Thawonmas, "A fuzzy classifier with ellipsoidal regions," IEEE Trans. Fuzzy Syst., vol. 5, pp. 358-368, Aug. 1997.
2. Y. R. Asfour, G. A. Carpenter, S. Grossberg, and G. W. Lesher, "Fusion ARTMAP: An adaptive fuzzy network for multi-channel classification," in Proc. World Congress on Neural Networks (WCNN-93), 1993, pp. 210-215.
3. A. Bargiela and G. D. Hainsworth, "Pressure and flow uncertainty in water systems," J. Water Resources Plan. Manage., vol. 115, no. 2, pp. 212-229, Mar. 1989.
4. A. Bargiela, "Operational decision support through confidence limits analysis and pattern classification," in Plenary Lecture, 5th Int. Conf. Computer Simulation and AI, Mexico, Feb. 2000.
5. R. E. Bellman, R. Kalaba, and L. A. Zadeh, "Abstraction and pattern classification," J. Math. Anal. Appl., vol. 13, pp. 1-7, 1966.
6. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Algorithms. New York: Plenum, 1981.
7. _, "Computing with uncertainty," IEEE Commun. Mag., pp. 24-36, Sept. 1992.
8. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Clarendon, 1995.
9. C. Blake, E. Keogh, and C. J. Merz. (1998) UCI repository of machine learning databases. Univ. Calif., Irvine. [Online] http://www.ics.uci.edu/∼mlearn/MLRepository.html
10. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, "Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps," IEEE Trans. Neural Networks, vol. 3, pp. 698-713, 1992.
11. G. A. Carpenter and S. Grossberg, "Fuzzy ARTMAP: A synthesis of neural networks and fuzzy logic for supervised categorization and non-stationary prediction," in Fuzzy Neural Networks and Soft Computing, R. R. Hager and L. A. Zadeh, Eds., 1994, pp. 126-166.
12. A. Cichocki and A. Bargiela, "Neural networks for solving linear inequality systems," Parallel Computing, vol. 22, no. 11, pp. 1455-1475.
13. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
14. B. Gabrys, "Neural network based decision support: Modeling and simulation of water distribution networks," Ph.D. dissertation, Nottingham Trent Univ., Nottingham, U.K., 1997.
15. G. Gabrys and A. Bargiela, "Neural networks based decision support in presence of uncertainties," J. Water Resources Plan. Manage., vol. 125, no. 5, pp. 272-280.
16. M. H. Hassoun, Fundamentals of Artificial Neural Networks. Cambridge, MA: MIT Press, 1995.
17. A. Joshi, N. Ramakrishman, E. N. Houstis, and J. R. Rice, "On neurobiological, neurofuzzy, machine learning, and statistical pattern recognition techniques," IEEE Trans. Neural Networks, vol. 8, Jan. 1997.
18. R. Krishnapuram and J. M. Keller, "A possible approach to clustering," IEEE Trans. Fuzzy Syst., vol. 1, pp. 98-110, May 1993.
19. R. Krishnapuram, "Generation of membership functions via possibilistic clustering," in Proc. 1994 IEEE 3rd Int. Fuzzy Systems Conf., vol. 2, June 1994, pp. 902-908.
20. D. Lowe and K. Zapart. (1998) Point-wise confidence interval estimation by neural networks: A comparative study based on automotive engine calibration. Tech. Rep. NCRG/98/007. [Online] http://www.ncrg.aston.ac.uk/
21. M. Meneganti, F. S. Saviello, and R. Tagliaferri, "Fuzzy neural networks for classification and detection of anomalies," IEEE Trans. Neural Networks, vol. 9, Sept. 1998.
22. S. Mitra and S. K. Pal, "Self-organizing neural network as a fuzzy classifier," IEEE Trans. Syst., Man, Cybern., vol. 24, Mar. 1994.
23. R. Moore, Interval Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1966.
24. O. Nasaroui and R. Krishnapuram, "An improved possibilistic C-means algorithm with finite rejection and robust scale estimation," in Proc. North American Fuzzy Information Processing, June 1996, pp. 395-399.
25. S. C. Newton, S. Pemmaraju, and S. Mitra, "Adaptive fuzzy leader clustering of complex data sets in pattern recognition," IEEE Trans. Neural Networks, vol. 3, pp. 794-800, Sept. 1992.
26. W. Pedrycz, "Fuzzy sets in pattern recognition: Methodology and methods," Pattern Recognit., vol. 23, no. 1/2, pp. 121-146, 1990.
27. _, "Fuzzy neural networks with reference neurons as pattern classifiers," IEEE Trans. Neural Networks, vol. 3, Sept. 1992.
28. W. Pedrycz and J. Waletzky, "Fuzzy clustering with partial supervision," IEEE Trans. Syst., Man, Cybern., vol. 27, pp. 787-795, Oct. 1997.
29. P. K. Simpson, Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations. New York: Pergamon, 1990.
30. _, "Fuzzy min-max neural networks - Part 1: Classification," IEEE Trans. Neural Networks, vol. 3, pp. 776-786, Sept. 1992.
31. _, "Fuzzy min-max neural networks - Part 2: Clustering," IEEE Trans. Fuzzy Syst., vol. 1, pp. 32-45, Feb. 1993.
32. R. R. Yager and L. A. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing. New York: Van Nostrand Reinhold, 1994.