Rule weights in a neuro-fuzzy system with a hierarchical domain partition

• Autorzy: Krzysztof Simiński
• Języki publikacji: EN

Abstrakty

EN

The paper discusses the problem of rule weight tuning in neuro-fuzzy systems with parameterized consequences in which rule weights and the activation of the rules are not interchangeable. Some heuristic methods of rule weight computation in neuro-fuzzy systems with a hierarchical input domain partition and parameterized consequences are proposed. Several heuristics with experimental results showing the advantage of their usage are presented.

Słowa kluczowe

EN

fuzzy inference system; hierarchical input domain partition; rule weights

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2010
• Tom: 20
• Numer: 2
• Strony: 337-347

Daty

wydano

2010

otrzymano

2009-01-21

poprawiono

2009-08-27

poprawiono

2009-10-10

Twórcy

autor

Krzysztof Simiński

• Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

• Abonyi, J., Babuška, R. and Szeifert, F. (2002). Modified GathGeva fuzzy clustering for identification of Takagi-Sugeno fuzzy models, IEEE Transactions on Systems, Man, and Cybernetics, Part B 32(5): 612-621.
• Almeida, M. R.A. (2004). Hybrid Neuro-Fuzzy-Genetic System for Automatic Data Mining, Pontifical Catholic University of Rio de Janeiro, (in Portugese).
• Berg, J.V.D., Kaymak, U. and van den Bergh, W.-M. (2002). Fuzzy classification using probability-based rule weighting, FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, Honolulu, HI, USA, Vol. 2, pp. 991-996.
• Box, G. E. P. and Jenkins, G. (1970). Time Series Analysis, Forecasting and Control, Holden-Day, Oakland, CA.
• Byun, Y. B., Takama, Y. and Hirota, K. (2001). Design of a modified T-S fuzzy model by adding compensation-rules, Journal of Japan Society for Fuzzy Theory and Systems 13(3): 98-109.
• Chen, J.-Q., Xi, Y.-G. and Zhang, Z.-J. (1998). A clustering algorithm for fuzzy model identification, Fuzzy Sets and Systems 98(3): 319-329.
• Chiu, S.L. (1994). Fuzzy model identification based on cluster estimation, Journal of Intelligent and Fuzzy Systems 2(3): 267-278.
• Cordón, O., del Jesus, M. and Herrera, F. (1999). A proposal on reasoning methods in fuzzy rule-based classification systems, International Journal of Approximate Reasoning 20(1): 21-45.
• Czekalski, P. (2006). Evolution-fuzzy rule based system with parameterized consequences, International Journal of Applied Mathematics and Computer Science 16(3): 373-385.
• Czogała, E. and Łęski, J. (2000). Fuzzy and Neuro-Fuzzy Intelligent Systems, Series in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg/New York, NY.
• Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters, Journal of Cybernetics 3(3): 32-57.
• Ferguson, D. E. (1960). Fibonaccian searching, Communications ACM 3(12): 648.
• Gath, I. and Geva, A.B. (1989). Unsupervised optimal fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7): 773-780.
• Glass, L. and Mackey, M.C. (1988). From Clocks to Chaos, the Rhythms of Life, Princeton University Press, Princeton, NJ.
• Gómez-Skarmeta, A.F., Delgado, M. and Vila, M.A. (1999). About the use of fuzzy clustering techniques for fuzzy model identification, Fuzzy Sets and Systems 106(2): 179-188.
• Ishibuchi, H. and Nakashima, T. (2001). Effect of rule weights in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems 9(4): 506-515.
• Ishibuchi, H. and Yamamoto, T. (2005). Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems 13(4): 428-435.
• Ishibuchi, H., Yamamoto, T. and Nakashima, T. (2001). Determination of rule weights of fuzzy association rules, 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, Vol. 3, pp. 1555-1558.
• Jahromi, M.Z. and Taheri, M. (2008). A proposed method for learning rule weights in fuzzy rule-based classification systems, Fuzzy Sets and Systems 159(4): 449-459.
• Jang, J.-S.R. (1993). ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics 23(3): 665-684.
• Joo, Y.H., Hwang, H.S., Kim, K.B. and Woo, K.B. (1997). Fuzzy system modeling by fuzzy partition and GA hybrid schemes, Fuzzy Sets and Systems 86(3): 279-288.
• Kim, E., Park, M., Ji, S. and Park, M. (1997). A new approach to fuzzy modeling, IEEE Transactions on Fuzzy Systems 5(3): 328-337.
• Kim, E., Park, M., Kim, S. and Park, M. (1998). A transformed input-domain approach to fuzzy modeling, IEEE Transactions on Fuzzy Systems 6(4): 596-604.
• Knuth, D.E. (1998). Art of Computer Programming, Volume 3: Sorting and Searching, 2nd Edition, Addison-Wesley Professional, Reading, MA.
• Larminat, P. and Thomas, Y. (1983). Control Engineering-Linear Systems, Wydawnictwa Naukowo-Techniczne, Warsaw, (in Polish).
• Lee, Y.-C., Hwang, E. and Shih, Y.-P. (1994). A combined approach to fuzzy model identification, IEEE Transactions on Systems, Man and Cybernetics 24(5): 736-744.
• Lin, Y. and Cunningham, G.A., I. (1995). A new approach to fuzzy-neural system modeling, IEEE Transactions on Fuzzy Systems 3(2): 190-198.
• Łęski, J. (2008). Neuro-Fuzzy Systems, Wydawnictwa NaukowoTechniczne, Warsaw, (in Polish).
• Łęski, J. and Czogała, E. (1997). A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications, BUSEFAL 71: 72-81.
• Łęski, J. and Czogała, E. (1999). A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications, Fuzzy Sets and Systems 108(3): 289-297.
• Makridakis, S.G., Wheelwright, S.C. and Hyndman, R.J. (1998). Forecasting: Methods and Applications, 3rd Edn., Wiley, New York, USA.
• Mamdani, E.H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies 7(1): 1-13.
• Nauck, D. (2000). Adaptive rule weights in neuro-fuzzy systems, Neural Computing and Applications 9(1): 60-70.
• Nauck, D. and Kruse, R. (1998). How the learning of rule weights affects the interpretability of fuzzy systems, Proceedings of the 1998 IEEE International Conference on Fuzzy Systems, Anchorage, AK, USA, Vol. 2, pp. 1235-1240.
• Nelles, O., Fink, A., Babuška, R. and Setnes, M. (2000). Comparison of two construction algorithms for Takagi-Sugeno fuzzy models, International Journal of Applied Mathematics and Computer Science 10(4): 835-855.
• Nelles, O. and Isermann, R. (1996). Basis function networks for interpolation of local linear models, Proceedings of the 35th IEEE Conference on Decision and Control, Cobe, Japan, Vol. 1, pp. 470-475.
• Nie, J. (1995). Constructing fuzzy model by self-organizing counterpropagation network, IEEE Transactions on Systems, Man and Cybernetics 25(6): 963-970.
• Nowicki, R. (2006). Rough-neuro-fuzzy system with MICOG defuzzification, 2006 IEEE International Conference on Fuzzy Systems, Vancouver, Canada, pp. 1958-1965.
• Nozaki, K., Ishibuchi, H. and Tanaka, H. (1996). Adaptive fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems 4(3): 238-250.
• Oh, S. and Pedrycz, W. (2000). Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems, Fuzzy Sets and Systems 115(2): 205-230.
• Pedrycz, W. (1984). An identification algorithm in fuzzy relational system, Fuzzy Sets and Systems 13(2): 153-167.
• Pedrycz, W., Lam, P. and Rocha, A.F. (1995). Distributed fuzzy system modelling, IEEE Transactions on System, Man and Cybernetics 25(5): 769-780.
• Priyono, A., Ridwan, M., Alias, A.J., Atiq, R., Rahmat, K., Hassan, A. and Mohd.Ali, M.A. (2005). Generation of fuzzy rules with subtractive clustering, Jurnal Teknologi, Series D 43: 143-153.
• Rantala, J. and Koivisto, H. (2002). Optimised subtractive clustering for neuro-fuzzy models, 3rd WSEAS International Conference on Fuzzy Sets and Fuzzy Systems, Interlaken, Switzerland.
• Simiński, K. (2008a). Neuro-fuzzy system with hierarchical domain partition, Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2008), Vienna, Austria, pp. 392-397.
• Simiński, K. (2008b). Two ways of domain partition in fuzzy inference system with parametrized consequences: Clustering and hierarchical split, OWD'2008: 10th International Ph.D. Workshop, Wisła, Poland, pp. 103-108.
• Simiński, K. (2009a). Patchwork neuro-fuzzy system with hierarchical domain partition, in M. Kurzyński and M. Woźniak (Eds.), Computer Recognition Systems 3, Advances in Intelligent and Soft Computing, Vol. 57, Springer-Verlag, Berlin/Heidelberg, pp. 11-18.
• Simiński, K. (2009b). Remark on membership functions in neuro-fuzzy systems, in K.A. Cyran, S. Kozielski, J.F. Peters, U. Stańczyk and A. Wakulicz-Deja (Eds.), Proceedings of the International Conference on ManMachine Interactions ICMMI 2009, Springer-Verlag, Berlin/Heidelberg, pp. 291-297.
• Souza, F.J.D., Vellasco, M.B.R. and Pacheco, M.A.C. (2002a). Load forecasting with the hierarchical neuro-fuzzy binary space partitioning model, International Journal of Computers, Systems and Signals 3(2): 118-132.
• Souza, F.J.D., Vellasco, M.M.R. and Pacheco, M.A.C. (2002b). Hierarchical neuro-fuzzy quadtree models, Fuzzy Sets and Systems 130(2): 189-205.
• Sugeno, M. and Kang, G.T. (1988). Structure identification of fuzzy model, Fuzzy Sets and Systems 28(1): 15-33.
• Sugeno, M. and Yasukawa, T. (1993). A fuzzy-logic-based approach to qualitative modeling, IEEE Transactions on Fuzzy Systems 1(1): 7-31.
• Surmann, H., Kanstein, A. and Goser, K. (1993). Self-organizing and genetic algorithms for an automatic design of fuzzy control and decision systems, Proceedings of the European Symposium on Intelligent Technology and Soft Computing EUFIT'93, Aachen, Germany, pp. 1097-1104.
• Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man and Cybernetics 15(1): 116-132.
• Tong, R.M. (1980). The evaluation of fuzzy models derived from experimental data, Fuzzy Sets and Systems 4(1): 1-12.
• Wang, J.-S. and Lee, C.S.G. (2002). Self-adaptive neurofuzzy inference systems for classification applications, IEEE Transactions on Fuzzy Systems 10(6): 790-802.
• Wang, L.-X. and Mendel, J. (1992). Generating fuzzy rules by learning from examples, IEEE Transactions on Systems, Man and Cybernetics 22(6): 1414-1427.
• Xu, C. W. and Lu, Y.Z. (1987). Fuzzy model identification selflearning for dynamic system, IEEE Transactions on Systems, Man and Cybernetics 17(9): 683-689.
• Yoshinari, Y., Pedrycz, W. and Hirota, K. (1993). Construction of fuzzy models through clustering techniques, Fuzzy Sets and Systems 54(2): 157-165.

Identyfikatory

DOI

10.2478/v10006-010-0025-3