Abstract
In this paper, we discuss fuzzy systems with a learning capability that realize high speed training and high generalization ability. First fuzzy classifiers with ellipsoidal regions, hyperbox regions, and polyhedron regions are discussed and their performance and that of the neural network classifier are compared. Then the rule extraction for the fuzzy classifiers is extended to function approximation. Finally performance of one fuzzy system for a water purification plant is compared with that of the neural network.
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References
S. Abe, “Neural Networks and Fuzzy Systems: Theory and Applications” Kindai Kagaku Sha, Tokyo, 1995 (in Japanese).
S. Abe and R. Thawonmas, “Fast Training of a Fuzzy Classifier with Ellipsoidal Regions,” submitted to Fifth IEEE International Conference on Fuzzy Systems, New Orleans, September 1996.
P. K. Simpson, “Fuzzy Min-Max Neural Networks — Part 1: Classification,” IEEE Trans. Neural Networks, Vol. 3, No. 5, pp. 776–786, Sept. 1992.
L.-X. Wang and J. M. Mendel, “Generating Fuzzy Rules by Learning from Examples,” IEEE Trans. Systems, Man, and Cybernetics, Vol. 22, No. 6, pp. 1414–1427, Nov/Dec 1992.
S. Abe and M.-S. Lan, “A Method for Fuzzy Rules Extraction Directly from Numerical Data and Its Application to Pattern Classification,” IEEE Trans. Fuzzy Systems, pp. 18–28, February 1995.
F. Uebele, S. Abe and M.-S. Lan, “A Neural Network-Based Fuzzy Classifier,” IEEE Trans. Systems, Man, and Cybernetics, Vol. 25, No. 2, pp. 353–361, February 1995.
M. T. Musavi, W. Ahmed, K. H. Chan, K. B. Faris, and D. M. Hummels, “On the Training of Radial Basis Function Classifiers,” Neural Networks, Vol. 5, No. 4, pp. 595–603, 1992.
S. Abe and M-S Lan, “Fuzzy Rules Extraction Directly from Numerical Data for unction Approximation,” IEEE Trans. Syst., Man, Cybern, Vol. 25, No. 1, 1995.
S. L. Chiu, “Fuzzy Model Identification Based on Cluster Estimation,” J. Intelligent and Fuzzy Systems, Vol. 2, pp. 267–278, 1994.
R. Fisher, “The Use of Multiple Measurements in Taxonomic Problems,” Annals of Eugenics, Vol. 7, Part II, pp. 179–188, 1936.
S. M. Weiss and I. Kapouleas, “An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods,” Proc. IJCAI-89, pp. 781–787, 1989.
A. Hashizume, J. Motoike, and R. Yabe, “Fully Automated Blood Cell Differential System and Its Application,” Proc. IUPAC 3rd International Congress on Automation and New Technology in the Clinical Laboratory, pp. 297–302, September 1988.
K. Baba, I. Enbutsu, and M. Yoda, “Explicit Representation of Knowledge Acquired from Plant Historical Data Using Neural Network,” Proc. IJCNN-90, San Diego, Vol. 3, pp. 155–160, June 17–21, 1990.
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© 1997 Springer-Verlag Berlin Heidelberg
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Abe, S. (1997). Fuzzy systems with learning capability. In: Martin, T.P., Ralescu, A.L. (eds) Fuzzy Logic in Artificial Intelligence Towards Intelligent Systems. FLAI 1995. Lecture Notes in Computer Science, vol 1188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62474-0_8
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DOI: https://doi.org/10.1007/3-540-62474-0_8
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