By D. Dubois, H. Prade, L. Ughetto (auth.), Nikhil R. Pal, Michio Sugeno (eds.)
It is our nice excitement to welcome you all to the 2002 AFSS foreign convention on Fuzzy platforms (AFSS 2002) to be held in Calcutta, the nice urban of pleasure. AFSS 2002 is the ?fth convention within the sequence initiated by means of the Asian Fuzzy structures Society (AFSS). AFSS 2002 is together being geared up by way of theIndianStatisticalInstitute(ISI)andJadavpurUniversity(JU). Likeprevious conferencesinthisseries,wearesure,AFSS2002willprovideaforumforfruitful interplay and trade of principles among the members from everywhere in the globe. the current convention covers all significant aspects of sentimental computing comparable to fuzzy good judgment, neural networks, genetic algorithms together with either theories and purposes. Wehopethismeetingwillbeenjoyableacademicallyandotherwise. we're grateful to the participants of the overseas application Committee and the realm Chairs for extending their aid in numerous varieties to make a robust technical software. every one submitted paper used to be reviewed by way of not less than 3 referees, and from time to time the revised types have been back checked by way of the ref- ees. due to this hard screening technique lets opt for purely approximately 50% of the submitted papers. We back show our honest due to all referees for doing an outstanding task. we're satisfied to notice that 19 di?erent nations from all around the globe are represented via the authors, thereby making it a really inter- tional convention. we're proud to have an inventory of unique audio system together with Profs. Z. Pawlak, J. Bezdek, D. Dubois, and T. Yamakawa.
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Additional info for Advances in Soft Computing — AFSS 2002: 2002 AFSS International Conference on Fuzzy Systems Calcutta, India, February 3–6, 2002 Proceedings
271-276. 7. 47-54. 8. , Fuzzy Sets and Fuzzy Logic: Theory and Applications,(Prentice Hall, New Jersey, 1995). 9. , ‘Fuzziness vs. Probability’, Int. J. of General Systems, Vol. 17, (1990), pp. 211-240. 10. 331-336 11. , ‘Features of Similarity’, Psychological Rev. 84(4), (1977), pp. 327-353. 12. , ‘Proximity Relations in The Fuzzy Relational Database Model’, Fuzzy Sets and Systems, 31, (1989), pp. 285-296 13. , ‘Ordinal Measures of Speciﬁcity’, Int. J. 17, (1990), pp. 57-72. 14. , ‘Similarity Relations and Fuzzy Orderings ’, Inform.
Thus, from the above facts, we may make an inference X0 is A , the justiﬁcations being the same. It has already been discussed that in such a situation S(A , A) ≤ S(A , A). Assume that 0 < S(A , A) ≤ S(A , A) ≤ 1. S(A , A); 0 ≤ r ≤ 1 and hence C ≤ C . Thus more knowledge about the prerequisite does not always guarantee more knowledge about the inference, purely non-monotonic. Fuzzy Set in Default Reasoning 33 Let us now consider a simple rule of the form — A → B and a default rule A →def B together with the values A and A is λ certain.
2000. 7. A. De Luca and S. Termini, “A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory,” Information and Control, Vol. 301-312, 1972. 8. M. Mizumoto, “Fuzzy Control Under Various Approximate Reasoning Methods,” Proc. 143-146, 1987. jp Abstract. In this paper, we discuss preciseness of data in terms of obtaining degree of similarity in which fuzzy set can be used as an alternative to represent imprecise data. Degree of similarity between two imprecise data represented in two fuzzy sets is approximately determined by using fuzzy conditional probability relation.
Advances in Soft Computing — AFSS 2002: 2002 AFSS International Conference on Fuzzy Systems Calcutta, India, February 3–6, 2002 Proceedings by D. Dubois, H. Prade, L. Ughetto (auth.), Nikhil R. Pal, Michio Sugeno (eds.)