[en] In direct approach to fuzzy modeling, structure identification is one of the most critical tasks. In modeling the nonlinear system, this fact is more crucial. In this paper, a new hybrid method is proposed to cluster the data located in the linear parts on the nonlinear systems. The proposed method can partition the input–output data in two groups: data located in the linear parts and data in the extrema. It is shown that the first group of data is suitable to be clustered by Fuzzy C-Regression Model (FCRM) clustering algorithm and the second group by Fuzzy C-Means (FCM). Then, based on the above findings, a new hybrid clustering algorithm is proposed. Finally, the proposed approach is tested and validated by several numerical examples of nonlinear functions.
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Bibliography
Chen, M.Y., Linkens, D.A., Rule-base self-generation and simplification for data-driven fuzzy models (2004) Fuzzy Sets and Systems, 142, pp. 243-265
Chuang, C.C., Su, S.F., Chen, S.S., Robust TSK fuzzy modeling for function approximation with outliers (2001) IEEE Transactions on Fuzzy Systems, 9 (6), pp. 810-821
Delgado, M., Gomez-Skarmeta, A.F., Martin, F., A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling (1997) IEEE Transactions on Fuzzy Systems, 5 (2), pp. 223-233
Emami, M.R., Turksen, I.B., Goldenberg, A.A., Development of a systematic methodology of fuzzy logic modeling (1998) IEEE Transactions on Fuzzy Systems, 6 (3), pp. 346-361
Espinosa, J., Vandewalle, J., Constructing fuzzy models with linguistic integrity from numerical data - AFRELI algorithm (2000) IEEE Transactions on Fuzzy Systems, 8 (5), pp. 591-600
Hoppner, F., Klawonn, F., Kruse, R., Runkler, T., (1999) Fuzzy Cluster Analysis, , John Wiley & Sons Chichester
Jin, Y., Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement (2000) IEEE Transactions on Fuzzy Systems, 8 (2), pp. 212-221
Kang, S.J., Woo, C.H., Hwang, H.S., Woo, K.B., Evolutionary design of fuzzy rule base for nonlinear system modeling and control (2000) IEEE Transactions on Fuzzy Systems, 8 (1), pp. 37-45
Kim, E., Park, M., Ji, S., Park, M., A new approach to fuzzy modeling (1997) IEEE Transactions on Fuzzy Systems, 5 (3), pp. 328-337
Kosko, B., (1997) Fuzzy Engineering, , Prentice Hall International New Jersey
Kosko, B., Optimal fuzzy rules cover extrema (1995) International Journal of Intelligent Systems, 10 (2), pp. 249-255
Reyes, C.N.P., (2004) Coevolutionary Fuzzy Modeling, , Springer-Verlag Berlin
Ross, T.J., (2004) Fuzzy Logic with Engineering Applications, , John Wiley & Sons Chichester
Setnes, M., Babuska, R., Kaymak, U., Lemke, H.R.N., Similarity measures in fuzzy rule base simplification (1998) IEEE Transactions on Systems, Man, and Cybernetics, 28 (3), pp. 376-386
Sugeno, M., Yasukawa, T., A fuzzy-logic-based approach to qualitative modeling (1993) IEEE Transactions on Fuzzy Systems, 1 (1), pp. 7-31
Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Transactions on Systems, Man, and Cybernetics, 15 (1), pp. 116-132
Tikk, D., Biro, G., Gedeon, T.D., Koczy, L.T., Yang, J.D., Improvements and critique on Sugeno's and Yasukawa's qualitative modeling (2002) IEEE Transactions on Fuzzy Systems, 10 (5), pp. 596-606
Wang, L.X., Mendel, J.M., Generating fuzzy rules by learning from examples (1992) IEEE Transactions on Systems, Man, and Cybernetics, 22 (6), pp. 1414-1427
Wolkenhauer, O., (2001) Data Engineering: Fuzzy Mathematics in Systems Theory and Data Analysis, , John Wiley & Sons New York
Wong, C.C., Chen, C.C., A hybrid clustering and gradient descent approach for fuzzy modeling (1999) IEEE Transactions on Systems, Man, and Cybernetics, 29 (6), pp. 686-693
Xing, Z.Y., Hu, W.L., Jia, L.M., A fuzzy clustering based approach for generating interpretable fuzzy models (2004) Proceedings of the Third International Conference on Machine Learning and Cybernetics, pp. 2093-2097. , Shanghai, China, August 2004
Yager, R.R., Filev, D.P., Unified structure and parameter identification of fuzzy models (1993) IEEE Transactions on Systems, Man, and Cybernetics, 23 (4), pp. 1198-1205
Zhao, J., Wertz, V., Gorez, R., A fuzzy clustering method for identification of fuzzy models for dynamic systems (1994) IEEE International Symposium on Intelligent Control, pp. 172-177. , OH, USA, August 1994
Zimmermann, H.J., (1996) Fuzzy Set Theory and Its Applications, , Kluwer Academic Publishers Boston
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