Context adaptation of Mamdani fuzzy rule based systems

International Journal of Intelligent Systems

Volumn 23, Issue 4, 2008, Pages 397-418

  Botta, Alessio ^a   Lazzerini, Beatrice ^b   Marcelloni, Francesco ^b  

^a IMT SCHOOL FOR ADVANCED STUDIES LUCCA   (Italy)

^b Dipartimento di Ingegneria dell'Informazione   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; DATA PROCESSING; HEURISTIC METHODS; KNOWLEDGE ACQUISITION; OPTIMIZATION;

CONTEXT ADAPTATION; HEURISTIC KNOWLEDGE;

FUZZY RULES;

EID: 42049102580     PISSN: 08848173     EISSN: 1098111X     Source Type: Journal    
DOI: 10.1002/int.20273     Document Type: Article

References (25)

1. Pedrycz W. Fuzzy sets engineering. Boca Raton, FL: CRC Press; 1995.
2. Ross TJ. Fuzzy logic with engineering applications, 2nd edition. Chichester, England: Wiley; 2004.
3. Pedrycz W, Gudwin RR, Gomide F. Nonlinear context adaptation in the calibration of fuzzy sets. Fuzzy Sets Syst 1997;88(1):91-97.
4. Gudwin RR, Gomide F. Context adaptation in fuzzy processing. In: Proc Brazil-Japan Joint Symp on Fuzzy Syst, Campinas, Brazil; 1994. pp 15-20.
5. Bastian A. How to handle the flexibility of linguistic variables with applications. Int J Uncertainty Fuzziness Knowledge Based Syst 1994;2(4):463-484.
6. Gudwin RR, Gomide F, Pedrycz W. Context adaptation in fuzzy processing and genetic algorithms. Int J Intell Syst 1998;13(10/11):929-948.
7. Magdalena L. Adapting the gain of an FLC with genetic algorithms. Int J Approx Reas 1997;17(4):327-349.
8. Cordón O, Herrera F, Magdalena L, Villar P. A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base. Inform Sci 2001;136(1-4):85-107.
9. Klawonn F. Reducing the number of parameters of a fuzzy system using scaling functions. Soft Comput 2006;10:749-756.
10. Magdalena L. On the role of context in hierarchical fuzzy controllers. Int J Intell Syst 2002; 17(5):471-493.
11. de Oliveira JV. Semantic constraints for membership function optimization. IEEE Trans Syst Man Cybernet A 1999;29(1):128-138.
12. Casillas J, Cordón O, Herrera F, Magdalena L, editors. Interpretability issues in fuzzy modeling. Berlin, Germany: Springer-Verlag; 2003.
13. Botta A, Lazzerini B, Marcelloni F. Context adaptation of Mamdani fuzzy systems through new operators tuned by a genetic algorithm. In: Proc 2006 Int Conf on Fuzzy Syst (FUZZ-IEEE'06), Vancouver, Canada; 2006. pp 7832-7839.
14. De Cock M, Kerre EE. A context-based approach to linguistic hedges. Int J Appl Math Comput Sci 2002; 12(3):371-382.
15. De Cock M, Kerre EE. A new class of fuzzy modifiers. In: Proc 30th IEEE Int Symp on Multiple-Valued Logic (ISMVL 2000), Portland, OR; 2000. pp 121-126.
16. Wang LX, Mendel JM. Generating fuzzy rules by learning from examples. IEEE Trans Syst, Man Cybernet 1992;22:1414-1427.
17. Alcalà R, Casillas J, Cordón O, Herrera F. COR: A methodology to improve ad hoc datadriven linguistic rule learning methods by inducing cooperation among rules. IEEE Trans Syst, Man Cybernet B 2002;32(4):526-537.
18. Marìn Blàzquez JG, Shen Q. Regaining comprehensibility of approximative fuzzy models via the use of linguistic hedges. In: Casillas J, Cordón O, Herrera F, Magdalena L, editors. Interpretability issues in fuzzy modeling. Berlin, Germany: Springer-Verlag; 2003.
19. Casillas J, Cordón O, del Jesus MJ, Herrera F. Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans Fuzzy Syst 2005;13(1):13-29.
20. Alcalá R, Herrera F. Genetic tuning on fuzzy systems based on the linguistic 2-tuples representation. In: Proc 2004 Int Conf on Fuzzy Syst (FUZZ-IEEE'04), Budapest, Hungary; 2004. pp 233-238.
21. Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst, Man Cybernet 1973;3(1):28-44.
22. Shi H, Ward R, Kharma N. Expanding the definitions of linguistic hedges. In: Proc Joint 9th IFSA World Congress and 20th NAFIPS Int Conf, Vancouver, Canada; 2001. Vol. 5, pp 2591-2595.
23. Michalewicz Z. Genetic algorithms + data structures = evolution programs, 3rd edition. Berlin, Germany: Springer-Verlag; 1999.
24. Cordon O, Gomide F, Herrera F, Hoffmann F, Magdalena L. Ten years of genetic fuzzy systems: Current framework and new trends. Fuzzy Sets Syst 2004;141(1):5-31.
25. Johnson RW. 2004 New car and truck data, http://www.amstat.org/ publications/jse; 2004.