Download Granular, Soft and Fuzzy Approaches for Intelligent Systems: by Janusz Kacprzyk, Dimitar Filev, Gleb Beliakov PDF

By Janusz Kacprzyk, Dimitar Filev, Gleb Beliakov

This e-book deals a entire record at the state-of-the paintings within the broadly-intended box of “intelligent systems”. After introducing key theoretical concerns, it describes a couple of promising types for facts and process research, choice making, and keep watch over. It discusses vital theories, together with risk conception, the Dempster-Shafer idea, the idea of approximate reasoning, in addition to computing with phrases, including novel functions in quite a few parts, equivalent to info aggregation and fusion, linguistic facts summarization, participatory studying, platforms modeling, and so forth. by means of featuring the equipment of their software contexts, the e-book exhibits how granular computing, tender computing and fuzzy common sense ideas delivers novel, effective options to real-world difficulties. it truly is devoted to Professor Ronald R. Yager for his nice medical and scholarly achievements, and for his long-lasting provider to the bushy common sense, and the substitute and computational intelligence groups. it's been inspired by means of the authors’ appreciation of his unique pondering and groundbreaking rules, with a unique concept to his beneficial learn at the automated implementation of varied points of human cognition for decision-making and problem-solving.

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Extra resources for Granular, Soft and Fuzzy Approaches for Intelligent Systems: Dedicated to Professor Ronald R. Yager

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Springer, Berlin (2007) 18. : Membership functions and probability measures of fuzzy sets. J. Am. Stat. Assoc. 99(467), 867–876 (2004) 19. : Didactical note: probabilistic conditionality in a boolean algebra. Mathware Soft Comput. 3(1–2), 149–157 (1996) 20. : On the words not-probable and improbable. In: Proceedings IPMU’2000, vol II, pp. 780–783. Madrid (2000) 21. : On a Model for the Meaning of Predicates. In: Seising, R. ) Views of Fuzzy Sets and Systems from Different Perspectives, pp. 175–205.

4 To finish, the mathematical study of fuzzy-fuzzy probability as a continuation of the classical theory of (crisp) probability still shows problems that should be clarified before talking of a theory of fuzzy probability. For what concerns the fuzzy-crisp probabilities in, at least, the definition of Zadeh, it seems that the algebras of fuzzy sets, like the triplets giving the Standard ones are not enough, but that families of more than three connectives are more suitable. Anyway, what it seems still lacking is an experimental study concerning the use in language of the linguistic label ‘probable’.

Inf. Process. Manage. 20(3), 363–372 (1984) 32. : Discussion: probability theory and fuzzy logic are complementary rather than competitive. Technometrics 37(3), 271–275 (1995) 33. : Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. J. Stat. Plann. Infer. 105, 233–264 (2002) 34. : Bayesianism—A brief revisit, e-mail to BISC Group, 2013/07/31 35. : Fuzzy Set Theory—and its Applications, Boston, Dordrecht. Kluver Academic Publ, London (1991) Organizing Families of Aggregation Operators into a Cube of Opposition Didier Dubois, Henri Prade and Agnès Rico Abstract The cube of opposition is a structure that extends the traditional square of opposition originally introduced by Ancient Greek logicians in relation with the study of syllogisms.

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