Toward a generalized theory of uncertainty (GTU) - an outline

By Lotfi A. Zadeh. IEEE International Conference on Granular Computing, 2005

628 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/3198237b7b343b66226f44ee236e11936467cf4f

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Citations in the corpus

Granular Computing: Perspectives and Challenges (2013, background): “Information granules permeate almost all human endeavors [1], [2], [7]–[11], [21], [63], [70], [116], [118], [119].”
A web based consensus support system for group decision making problems and incomplete preferences (2010, background): “This includes, but is not restricted to, the definition of the representation format(s) [45] available for the experts to express their preferences about the possible alternatives in the problem: linguistic [2,19,53] or fuzzy [32,33,51,62,63] formats.”
Soft semirings (2008, background): “Alternatively, mathematical theories such as probability theory, fuzzy set theory [1,2], rough set theory [3,4], vague set theory [5] and the interval mathematics [6] were established by researchers to modelling uncertainties appearing in the above fields.”
Relationship between generalized rough sets based on binary relation and covering (2009, background): “To solve this problem, scientists in computer science and related areas have proposed various theories such as fuzzy set theory [43,44], granular computing [1,13,30,33,34,39,40], and rough sets [6,19–22,27,48].”
Uncertainty measures for interval type-2 fuzzy sets (2007, neutral): “As pointed out by Zadeh [87], ‘‘ uncertainty is an attribute of information ’’.”
Hybrid fuzzy and optimal modeling for water quality evaluation (2007, background): “The centerpiece of GTU is the concept of a generalized constraint, a concept drawn from fuzzy logic [Zadeh, 2005].”
Soft sets and soft groups (2007, background): “This approach puts the generalized theory of uncertainty into a much broader perspective, as outlined in [27].”
Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP (2008, background): “Generally, these preference relations can be categorized into multiplicative preference relations [15,19,44], fuzzy preference relations [1,8–10,13,15,24,38,41–47] and linguistic preference relations [17,18,20–23,25,26,48–50,52].”
An approach to multiattribute decision making with interval-valued intuitionistic fuzzy assessments and incomplete weights (2009, background): “This extension has triggered signiﬁcant theoretical developments and numerous successful industrial applications [17,41], and provides a powerful alternative other than probability theory to characterize uncertainty, imprecision, and vagueness in many ﬁelds [40].”
Is there a need for fuzzy logic? (2008, background): “control ? fuzzy control [12,20] linear programming ? fuzzy linear programming [108,21] probability theory ? fuzzy probability theory [102,104,107] measure theory ? fuzzy measure theory [14,66] topology ? fuzzy topology [73,41] graph theory ? fuzzy graph theory [36,44] cluster analysis ? fuzzy cluste”
Uncertainty in flood damage estimates and its potential effect on investment decisions (2015, background): “However, there are alternative concepts to describe uncertainty, such as imprecise probabilities or Bayesian statistics, that deal with this problem (Reichert and Omlin, 1997; Zadeh, 2005).”
Testing fuzzy hypotheses using fuzzy data based on fuzzy test statistic (2011, background): “In addition, studying the problem of testing hypothesis within the framework of granular computing [20, 33] is a potential subject for further research.”
A NOTE ON SOFT NEAR-RINGS AND IDEALISTIC SOFT NEAR-RINGS (2011, background): “Thus, there have been a great deal of research and applications in the literature concerning some special tools like probability theory, fuzzy set theory [21] [22], rough set theory [15] [16], vague set theory [9], interval mathematics [5] [10], and intuitionistic fuzzy set theory [6] [23].”
Intuitionistic preference relations and their application in group decision making (2007, background): “; tg, where si represents a linguistic variable [43,63] and satisfies the following characteristics: (1) the set is ordered: si > sj if i > j; (2) there is a negation operator: neg ðstÞ 1⁄4 s t.”
Choquet integrals of weighted intuitionistic fuzzy information (2010, background): “Zadeh’s fuzzy sets [24–26] are a very popular tool to describe imprecise or vague information in a variety of applied fields [4] such as management, engineering, artificial intelligence, data mining, pattern recognition and soft computing.”