Uncertainty Data in Interval-Valued Fuzzy Set Theory: Properties, Algorithms and Applications: Studies in Fuzziness and Soft Computing, cartea 367
Autor Barbara Pękalaen Limba Engleză Hardback – 12 iul 2018
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Specificații
ISBN-13: 9783319939094
ISBN-10: 3319939092
Pagini: 173
Ilustrații: XIV, 181 p. 12 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.45 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Fuzziness and Soft Computing
Locul publicării:Cham, Switzerland
ISBN-10: 3319939092
Pagini: 173
Ilustrații: XIV, 181 p. 12 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.45 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Fuzziness and Soft Computing
Locul publicării:Cham, Switzerland
Cuprins
Introduction to Fuzzy Sets.- Interval-Valued Fuzzy Relations.- Applications.- Summary and Open Problem.
Textul de pe ultima copertă
This book offers an introduction to fuzzy sets theory and their operations, with a special focus on aggregation and negation functions. Particular attention is given to interval-valued fuzzy sets and Atanassov’s intuitionistic fuzzy sets and their use in uncertainty models involving imperfect or unknown information. The theory and application of interval-values fuzzy sets to various decision making problems represent the central core of this book, which describes in detail aggregation operators and their use with imprecise data represented as intervals. Interval-valued fuzzy relations, compatibility measures of interval and the transitivity property are thoroughly covered. With its good balance between theoretical considerations and applications of originally developed algorithms to real-world problem, the book offers a timely, inspiring guide to mathematicians and engineers developing new decision making models or implementing/applying existing ones to a wide range of applications involving imprecise or incomplete data.
Caracteristici
Focuses on interval-valued fuzzy aggregations, and its importance for solving multi criteria decision making problems Describes fuzzy set based methods for dealing with incomplete knowledge Highlights the case of imprecise data represented as intervals