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Improving Convergence of Parameter Tuning Process of the Additive Fuzzy System by New Learning Strategy
Abstract:An additive fuzzy system comprising m rules with n inputs and p outputs in each rule has at least t m(2n + 2 p + 1) parameters needing to be tuned. The system consists of a large number of if-then fuzzy rules and takes a long time to tune its parameters especially in the case of a large amount of training data samples. In this paper, a new learning strategy is investigated to cope with this obstacle. Parameters that tend toward constant values at the learning process are initially fixed and they are not tuned till the end of the learning time. Experiments based on applications of the additive fuzzy system in function approximation demonstrate that the proposed approach reduces the learning time and hence improves convergence speed considerably.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063208Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1273
 B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, 1991.
 B. Kosko, "Fuzzy systems as universal approximators", IEEE Transactions on Computers, vol. 43, no. 11, 1994, pp. 1329-1333.
 B. Kosko, "Combining fuzzy systems". Proceedings of the IEEE International Conference on Fuzzy Systems (IEEE FUZZ-95), 1995, pp. 1855-1863.
 B. Kosko, "Optimal fuzzy rules cover extrema". International Journal of Intelligent Systems, vol. 10, no. 2, 1995, pp. 249-255.
 B. Kosko, Fuzzy Engineering. Prentice Hall, 1996.
 B. Kosko, "Global stability of generalized additive fuzzy systems", IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, no. 3, 1998, pp. 441-452.
 G.J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, NJ, USA, 1995.
 J.A. Dickerson and B. Kosko, "Fuzzy function approximation with supervised ellipsoidal learning", World Congress on Neural Networks, vol. 2, 1993, pp. 9-13.
 J.A. Dickerson and B. Kosko, "Fuzzy function approximation with ellipsoidal rules", IEEE Transactions Systems, Man, and Cybernetics, vol. 26, no. 4, 1996, pp. 542-560.
 S. Mitaim and B. Kosko, "What is the best shape for a fuzzy set in function approximation?", Proceeding of the 5th IEEE International Conference on Fuzzy Systems, vol. 2, 1996, pp. 1237-1243.
 S. Mitaim and B. Kosko, "Adaptive joint fuzzy sets for function approximation", Proceeding of IEEE International Conference on Neural Networks, vol. 1, 1997, pp. 537-542.
 S. Mitaim and B. Kosko, "The shape of fuzzy sets in adaptive function approximation", IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, 2001, pp. 637-656.