Fuzzy Logic and Soft Computing Applications: 11th by Alfredo Petrosino, Vincenzo Loia, Witold Pedrycz

By Alfredo Petrosino, Vincenzo Loia, Witold Pedrycz

This e-book constitutes the complaints of the eleventh overseas Workshop on Fuzzy common sense and purposes, WILF 2016, held in Naples, Italy, in December 2016.

The 22 revised complete papers offered including 2 invited lectures have been conscientiously reviewed and chosen from quite a few submissions. The papers are equipped in topical sections on fuzzy measures and transforms; granularity and multi-logics, clustering and studying; wisdom platforms; and gentle computing and applications.

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57(4), 829–837 (2008) 15. : Shadowed sets: representing and processing fuzzy sets. IEEE Trans. Syst. Man Cybern. Part B 28, 103–109 (1998) 16. : Ordinal measures of specificity. Int. J. Gen. Syst. 17, 57–72 (1990) 17. : Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005) 18. : Granular computing: perspectives and challenges. IEEE Trans. Cybern. 43(6), 1977–1989 (2013) 19. : On fuzzy cluster validity indices. Fuzzy Sets Syst. 158(19), 2095– 2117 (2007) 20. : Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic.

Analysis and Design of Intelligent Systems using Soft Computing Techniques, vol. 41, pp. 16–25. Springer, Heidelberg (2007) 30. : Towards general type-2 fuzzy logic systems based on zSlices. IEEE Trans. Fuzzy Syst. 18(4), 637–660 (2010) 26 G. Ruiz-García et al. 31. : The algebra of fuzzy truth value. Fuzzy Sets Syst. 149, 309–347 (2005) 32. : The concept of linguistic variable and its application to approximate reasoning. Inf. Sci. 8(3), 199–249 (1975) 33. : A non-singleton interval type-2 fuzzy logic system for universal image noise removal using quantum-behaved particle swarm optimization.

Higher values of c underline the importance of specificity as a resulting A gets more specific. The result of optimization comes in the form bopt = arg maxb Q(b). The optimization of the lower bound of the fuzzy set (a) is carried out in an analogous way as above yielding aopt = arg Max Q(a). Several observations are worth making here. First, the approach exhibits a general character and the principle is applicable to any formalism of information granules; here we just highlighted the case of sets and fuzzy sets.

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