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Foundations of computational intelligence volume 2

approximate reasoning

Édité parAjith AbrahamFrancisco HerreraAboul-Ella Hassanien

Résumé

Human reasoning usually is very approximate and involves various types of uncertainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, and rough set, near set and hybrid intelligent systems. Besides research articles and expository papers on theory and algorithms of approximation reasoning, papers on numerical experiments and real world applications were also encouraged. This Volume comprises of 12 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for approximation reasoning. The Volume is divided into 2 parts: Part-I: Approximate Reasoning – Theoretical Foundations and Part-II: Approximate Reasoning – Success Stories and Real World Applications.

Détails | Table des matières

Fuzzy without fuzzy

why fuzzy-related aggregation techniques are often better even in situations without true fuzziness

Hung T NguyenVladik Kreinovich François ModaveMartine Ceberio

pp.27-51

https://doi.org/10.1007/978-3-642-01533-5_2

Détails de la publication

Maison d'édition: Springer

Lieu de publication: Dordrecht

Année: 2009

Pages: 310

Collection: Studies in Computational Intelligence


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: 202

DOI: 10.1007/978-3-642-01533-5

ISBN (hardback): 978-3-642-01532-8

ISBN (digital): 978-3-642-01533-5

Citation complète:

Abraham Ajith, Herrera Francisco, Hassanien Aboul-Ella (éd.), 2009, Foundations of computational intelligence volume 2: approximate reasoning. Dordrecht, Springer.