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Download Advances in Soft Computing — AFSS 2002: 2002 AFSS by D. Dubois, H. Prade, L. Ughetto (auth.), Nikhil R. Pal, PDF

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 through the Asian Fuzzy platforms Society (AFSS). AFSS 2002 is together being equipped by way of theIndianStatisticalInstitute(ISI)andJadavpurUniversity(JU). Likeprevious conferencesinthisseries,wearesure,AFSS2002willprovideaforumforfruitful interplay and trade of principles among the contributors from all around the globe. the current convention covers all significant aspects of sentimental computing equivalent to fuzzy common sense, 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 types to make a powerful technical application. every one submitted paper was once reviewed through no less than 3 referees, and every so often the revised types have been back checked through the ref- ees. because of this hard screening procedure lets decide on in basic terms approximately 50% of the submitted papers. We back convey our honest due to all referees for doing a very good activity. we're chuffed to notice that 19 di?erent international locations from all around the globe are represented by means of the authors, thereby making it a very inter- tional convention. we're proud to have an inventory of special audio system together with Profs. Z. Pawlak, J. Bezdek, D. Dubois, and T. Yamakawa.

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Extra info for Advances in Soft Computing — AFSS 2002: 2002 AFSS International Conference on Fuzzy Systems Calcutta, India, February 3–6, 2002 Proceedings

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On the other hand, (4) tells that empty class (the class or subset that has no any element of data) and universal class (the class which perfectly covers all elements of data) are meaningless, where universal class is considered as total ignorance in terms of preciseness of data (see Section 2). Example 3. 6/BA, 1/M S, 1/P hD}. 20). 24 R. Intan and M. Mukaidono Table 2. Similarity R(X, Y ) of Elements Table 1. 0 On the other hand, every crisp element of data in education can also be represented as fuzzy sets in terms of their fuzzy classes.

The possible certainty-value of a vague statement constitutes the continuum [0,1]. Thus, any linguistic certainty-value may be represented by a fuzzy set over the universe of discourse [0,1]. In the sequel, the statement ‘X is A, is certain’ may be taken as ‘X is A’ and the statement ‘X is A is uncertain’ may be taken as ‘X is U ’, the universe of discourse of the linguistic variable. Another important issue need to be considered in default reasoning is, a way of blocking undesirable transitivity which arises because of absence of an appropriate mechanism to incorporate explicit reference to the list of exceptional circumstances which would block its application.

Next, let us consider an interesting case, where, A ⊆ A ⊆ A holds. Here, X0 is A is more informative than X0 is A . 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.

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