A GA-based method for constructing TSK fuzzy rules from numerical data

IEEE International Conference on Fuzzy Systems

Volumn 1, Issue , 2003, Pages 131-134

  Kumar, Ashwani ^a   Agrawal, D P ^a   Joshi, S D ^b  

^a ABV INDIAN INSTITUTE OF INFORMATION TECHNOLOGY AND MANAGEMENT   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

Author keywords

Forecasting; Fuzzy modeling; Genetic algorithms; Knowledge acquisition; Time Series

Indexed keywords

BENCHMARKING; COMPUTER SIMULATION; FORECASTING; GENETIC ALGORITHMS; KNOWLEDGE ACQUISITION; KNOWLEDGE BASED SYSTEMS; LEAST SQUARES APPROXIMATIONS; LINGUISTICS; TIME SERIES ANALYSIS;

TAKAGI-SUGENO-KANG (TSK) FUZZY INFERENCE SYSTEM;

FUZZY SETS;

EID: 0038199533     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Takagi, T., and M.Sugeno, "Fuzzy identification of systems and its application to modeling and control," IEEE Trans. Syst., Man, Cybern., vol.SMC-15, pp.116-132, Jan/Feb.1985.
2. Sugeno, M., and G.T.Kang, "Structure identification of fuzzy model," Fuzzy Sets Syst., vol.28, pp.15-33, 1988.
3. Wang, L-X, and J.M.Mendel, "Generating fuzzy rules by learning from examples", IEEE Trans. Syst., Man Cyber., vol.22, no.6, pp.1414-1427, 1992.
4. Kosko, B., "Fuzzy systems as universal approximators", Proc. IEEE Int. Conf. Fuzzy Syst., pp.1153-1162, San Diego, 1992.
5. Juang, C-F, and C-T Lin, "An on-line self-constructing neural fuzzy inference network with its applications", IEEE Trans. Fuzzy Syst., vol.6, no.1, pp.12-32, 1998.
6. Sugeno, M., and T.Yasukawa, "A fuzzy-logic-based approach to qualitative modeling", IEEE Trans. Fuzzy Syst., vol.1, no.1, pp.7-31, 1993.
7. ezdek, J.C., and S.K.Pal (editors), Fuzzy Models for Pattern Recognition, New York: IEEE Press, 1992.
8. Tohonen, T., Self-Organizing Maps, 2nd edition, Springer Verlag, 1997.
9. Kim, E., Minker Park, S.Ji, and Mignon Park, "A new approach to fuzzy modeling", IEEE Trans. Fuzzy Syst., vol.5, no.3, pp.328-337, 1997.
10. in, C-T, and C.S.G Lee, Neural Fuzzy Systems: A Neural-Fuzzy Synergism to Intelligent Systems, Englewood Cliffs, NJ: Prentice-Hall, 1996.
11. Kim, J., and N.Kasobov, "HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems", Neural Networks, vol.12, pp.1301-1319, 1999.
12. Ishibuchi, H., and T.Nakashima, "Effect of the rule weights in fuzzy rule-based classification systems", IEEE Trans. Fuzzy Syst., vol.9, no.4, pp.506-515, 2001.
13. Cardon, O., F.Herrera, and P.Villar, "Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing," Int. J.Approx. Reason., vol.25, no.3, p.187-215, 2000.
14. Goldberg, D.E., Genetic Algorithms in Search, Optimization & Machine Learning, Reading, Mass.: Addison-Wesley, 1989.
15. Cordon, O., F.Herrera, and P.Villar, "Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base", IEEE Trans. Fuzzy Systems, vol.9, no.4, Aug.2001.
16. Wong, C-C, and C-C Chen, "A GA-based method for constructing fuzzy systems directly from numerical data", IEEE Trans. Systems, Man. Cyber - Part B: Cyber., vol.30, no.6, Dec.2001.
17. Box, G.E.P., and G.M.Jenkins, Time Series analysis, forecasting and control, San Francisco,CA: Holden day, 1970.
18. Ashwani Kumar, D. P. Agrawal and S. D. Joshi, " Neural Networks and Fuzzy Inference Systems for Business Forecasting", Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery, Singapore, FSKD" 02, Vol. 2, p.661-666