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

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

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 good urban of pleasure. AFSS 2002 is the ?fth convention within the sequence initiated by way of the Asian Fuzzy structures Society (AFSS). AFSS 2002 is together being equipped via theIndianStatisticalInstitute(ISI)andJadavpurUniversity(JU). Likeprevious conferencesinthisseries,wearesure,AFSS2002willprovideaforumforfruitful interplay and trade of rules among the contributors 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 functions. Wehopethismeetingwillbeenjoyableacademicallyandotherwise. we're grateful to the contributors of the overseas software Committee and the realm Chairs for extending their aid in a number of kinds to make a powerful technical application. each one submitted paper was once reviewed by means of at the least 3 referees, and now and again the revised models have been back checked through the ref- ees. due to this hard screening procedure lets decide upon simply approximately 50% of the submitted papers. We back convey our honest because of all referees for doing a good task. we're chuffed to notice that 19 di?erent nations from everywhere in the globe are represented via the authors, thereby making it a very inter- tional convention. we're proud to have a listing of individual audio system together with Profs. Z. Pawlak, J. Bezdek, D. Dubois, and T. Yamakawa.

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

Example text

Dn } can be determined: U Di = Z 0 . e. Di , a sub-rule base Ri can be constructed with local validity. In the worst case, each sub-rule base refers to exactly X Z0 = X p +1 × ... × X m The complexity of the whole rule base O(T m ) is not decreased, as the size of R0 is O(T p ) , and each Ri , i > 0, is of order O(T m − p ) , O(T p ) × O (T m − p ) = O(T m ) . , X m , so that each Ri contains only less than m − p input variables. The { } task of finding such a partition is often difficult, if not impossible, (sometimes such partition does not even exist).

On the other hand, an increasing number of elements in a class will reduce the degree of relationship (similarity) among the element involved in the class. Obviously Table 1 and 2 show that degrees of similarity between fuzzy classes as well as elements of data are not symmetric as a consequence of membership functions arbitrarily given in low edu, mid edu, and hi edu where |mid edu| < |low edu| < |hi edu|, so that |P hD| = |M S| < |ES| < |BA| < |JHS| < |SHS|. However, it depends on the type of data and application in which we may provide the same cardinality of fuzzy classes in partition by applying the following equation (5) instead of (4) in Definition 3.

5. The question here is which model is the most interpretable. The model in Fig. 5 (c) has equidistantly allocated membership functions. Although this model is less accurate, it is easier to get the rough idea about the input-output relationships from this model than from other models in Fig. 5. The average measure of the model in Fig. 5 (c) is the smallest, and this measure coincides with the observation of conciseness. 18 T. 00079 Fig. 5. Allocation of membership functions of obtained models on the Pareto front Fig.

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