Imprecise specification of ill-known functions using gradual rules

International Journal of Approximate Reasoning

Volumn 35, Issue 3, 2004, Pages 205-222

  Galichet, Sylvie ^a   Dubois, Didier ^b   Prade, Henri ^b  

^a Université de Savoie   (France)

^b UNIVERSITÉ PAUL SABATIER   (France)

Author keywords

Fuzzy data; Fuzzy interpolation; Fuzzy rules; Gradual rules; Imprecise interpolation

Indexed keywords

APPROXIMATION THEORY; GRAPH THEORY; INTERPOLATION; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE REPRESENTATION; MATHEMATICAL MODELS; POLYNOMIALS; THEOREM PROVING;

FUZZY DATA; FUZZY INTERPOLATION; FUZZY RULES; GRADUAL RULES; IMPRECISE INTERPOLATION;

FUZZY SETS;

EID: 1142303897     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2003.08.002     Document Type: Conference Paper

References (17)

1. Anile A.M., Falcidieno B., Gallo G., Spagnuolo M., Spinello S. Modeling uncertain data with fuzzy B-splines. Fuzzy Sets Syst. 113:2000;397-410.
2. B. Bouchon-Meunier, D. Dubois, C. Marsala, H. Prade, L. Ughetto, A comparative view of interpolation methods between sparse fuzzy rules, in: Proc. of the 9th IFSA World Congress, Vancouver, Canada, 2001, pp. 2499-2504.
3. Dubois D., Esteva F., Garcia P., Godo L., Prade H. A logical approach to interpolation based on similarity relations. Int. J. Approx. Reason. 17(1):1997;1-36.
4. Dubois D., Esteva F., Garcia P., Godo L., de Mantaras R.L., Prade H. Fuzzy set modelling in case-based reasoning. Int. J. Intell. Syst. 13(4):1998;345-373.
5. Dubois D., Hüllermeier E., Prade H. On the representation of fuzzy rules in terms of crisp rules. Inform. Sci. 151:2003;301-326.
6. Dubois D., Prade H. Gradual inference rules in approximate reasoning. Inform. Sci. 61(1-2):1992;103-122.
7. D. Dubois, H. Prade, Basic issues on fuzzy rules and their application to fuzzy control, in: D. Driankov, P.W. Eklund, A.L. Ralescu (Eds.), Fuzzy Logic and Fuzzy Control, Lecture Notes in Artificial Intelligence, 833, Springer-Verlag, 1994, pp. 3-14.
8. Dubois D., Grabisch M., Prade H. Gradual rules and the approximation of control laws. Nguyen H.T., Sugeno M., Tong R., Yager R.R. Theoretical Aspects of Fuzzy Control. 1995;147-181 John Wiley & Sons Inc. (Chapter 7).
9. Dubois D., Prade H., Ughetto L. Checking the coherence and redundancy of fuzzy knowledge bases. IEEE Trans. Fuzzy Syst. 5(3):1997;398-417.
10. S. Galichet, D. Dubois, H. Prade, Categorizing classes of signals by means of fuzzy gradual rules, Proc. of the 18th International Joint Conference on Artificial Intelligence (IJCAI'03), Acapulco, Mexico, August 2003, pp. 1039-1044.
11. S. Galichet, D. Dubois, H. Prade, Fuzzy interpolation and level 2 gradual rules, Proc. of the 3rd European Conference in Fuzzy Logic and Technology (EUSFLAT 2003), Zittau, Germany, September 2003, pp. 506-511.
12. Kaleva O. Interpolation of fuzzy data. Fuzzy Sets Syst. 61:1994;63-70.
13. E. Keogh, H. Hochheiser, B. Shneiderman, An augmented visual query mechanism for finding patterns in time series data, in: T. Andreasen, A. Motro, H. Christiansen, H.L. Larsen (Eds.), Proc. of the 5th Int. Conf. On Flexible Query Answering Systems (FQAS 2002), Lecture Notes in Artificial Intelligence, 2522, Springer-Verlag, Copenhagen, Denmark, October 2002, pp. 240-250.
14. Lowen R. A fuzzy Lagrange interpolation theorem. Fuzzy Sets Syst. 34:1990;33-38.
15. Sala A., Albertos P. Inference error minimisation: fuzzy modelling of ambiguous functions. Fuzzy Sets Syst. 121(1):2001;95-111.
16. Zadeh L. Fuzzy sets. Inform. Contr. 8:1965;338-353.
17. Zadeh L. Quantitative fuzzy semantics. Inform. Sci. 3(2):1971;159-176.