Abstract
The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuzzy sets for learning and classification. In this paper, we propose modifications to FMM in an attempt to improve its classification performance in situations when large hyperboxes are formed by the network. To achieve the goal, the Euclidean distance is computed after network training. We also propose to employ both the membership value of the hyperbox fuzzy sets and the Euclidean distance for classification. To assess the effectiveness of the modified FMM network, benchmark pattern classification problems are first used, and the results from different methods are compared. In addition, a fault classification problem with real sensor measurements collected from a power generation plant is used to evaluate the applicability of the modified FMM network. The results obtained are analyzed and explained, and implications of the modified FMM network in real environments are discussed.
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References
Al-Najjar, B.: Total quality maintenance: An approach for continuous reduction in costs of quality products. Journal of Quality in Maintenance Engineering 2, 2–20 (1996)
Devijver, P., Kittler, P.: Pattern Recognition: A Statistical Approach. Prentice-Hall, Englewood Cliffs (1982)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1972)
Gabrys, B., Bargiela, A.: General Fuzzy Min-Max Neural Network for Clustering and Classification. IEEE Trans on Neural Networks 11, 769–783 (2000)
Hoang, A.: Supervised Classifier Performance on the UCI Data Set. M.Sc. Thesis, Department of Computer Science, University of Adelaide, Australia (1997)
Huang, Y.C., Yang, H.T., Huang, K.Y.: Adductive network model-based diagnosis system for power transformer incipient fault-detection. IEE Proceedings- Generation, Transmission and Distribution 149, 326–330 (2002)
Kohonen, T.: Self-Organization and Associative Memory. Springer, Berlin (1984)
Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases (Machine-readable data repository). Department of information and computer Science. University of California, Irvine, CA (1995)
Nakashima, T., Nakai, G., Ishibuchi, H.: Constructing Fuzzy Ensembles for Pattern Classification Problems. In: IEEE SMC Int. Conf., vol. 4, pp. 3200–3205. IEEE, Los Alamitos (2003)
Simpson, P.: Fuzzy Min-Max Neural Networks–Part 1: Classification. IEEE Trans. on Neural Networks 3, 776–786 (1992)
Ster, B., Dobnikar, A.: Neural Network in medical diagnosis: Comparison with other methods. In: Bulsari, A., et al. (eds.) Proc. Int. Conf. EANN’96, pp. 427–430 (1996)
System description and operating procedures. Prai Power Station Stage 3, vol. 14 (1999)
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Quteishat, A.M., Lim, C.P. (2007). A Modified Fuzzy Min-Max Neural Network and Its Application to Fault Classification. In: Saad, A., Dahal, K., Sarfraz, M., Roy, R. (eds) Soft Computing in Industrial Applications. Advances in Soft Computing, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70706-6_17
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DOI: https://doi.org/10.1007/978-3-540-70706-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70704-2
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