Search Results for Fuzzy sets.

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Fuzzy Sets, Rough Sets, Multisets and Clustering

Torra, Vicenç. editor.

http://dx.doi.org/10.1007/978-3-319-47557-8

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Fuzzy Sets, Rough Sets, Multisets and Clustering Torra, Vicenç. editor.

Optical Character Recognition Systems for Different Languages with Soft Computing

Chaudhuri, Arindam. author.

http://dx.doi.org/10.1007/978-3-319-50252-6

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systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic

Type-2 Fuzzy Granular Models

Sanchez, Mauricio A. author.

http://dx.doi.org/10.1007/978-3-319-41288-7

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-type information granules represented by Interval Type-2 Fuzzy Sets are also shown, via multiple approaches, such

Claudio Moraga: A Passion for Multi-Valued Logic and Soft Computing

Seising, Rudolf. editor.

http://dx.doi.org/10.1007/978-3-319-48317-7

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Science: an Homage to Claudio Moraga -- Some reflections on the use of interval fuzzy sets for dealing

Intuitionistic Fuzzy Logics

T. Atanassov, Krassimir. author.

http://dx.doi.org/10.1007/978-3-319-48953-7

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-of-the-art applications of intuitionistic fuzzy sets. The book is a valuable reference resource for graduate students and

Design of Interpretable Fuzzy Systems

Cpałka, Krzysztof. author.

http://dx.doi.org/10.1007/978-3-319-52881-6

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demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction

Time-Series Prediction and Applications A Machine Intelligence Approach

Konar, Amit. author.

http://dx.doi.org/10.1007/978-3-319-54597-4

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Sets -- Handling Multiple Factors in the Antecedent of Type-2 Fuzzy Rules -- Learning Structures in an

Granular Neural Networks, Pattern Recognition and Bioinformatics

Pal, Sankar K. author.

http://dx.doi.org/10.1007/978-3-319-57115-7

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discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in

Issues in the Use of Neural Networks in Information Retrieval

Iatan, Iuliana F. author.

http://dx.doi.org/10.1007/978-3-319-43871-9

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based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm

Cover image for Quantitative Logic and Soft Computing 2016 Proceedings of the 4th International Conference on Quantitative Logic and Soft Computing (QLSC2016) held at Hangzhou, China, 14-17 October, 2016

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Quantitative Logic and Soft Computing 2016 Proceedings of the 4th International Conference on Quantitative Logic and Soft Computing (QLSC2016) held at Hangzhou, China, 14-17 October, 2016

Fan, Tai-He. editor.

http://dx.doi.org/10.1007/978-3-319-46206-6

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Quantification of Software; (3) Fuzzy Connectives and Fuzzy Reasoning; (4) Fuzzy Logical Algebras; (5) Artificial

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Uncertain Rule-Based Fuzzy Systems Introduction and New Directions, 2nd Edition

Mendel, Jerry M. author.

http://dx.doi.org/10.1007/978-3-319-51370-6

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-based techniques for handling real-world uncertainties; Allows those already familiar with type-1 fuzzy sets and

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On the Logos: A Naïve View on Ordinary Reasoning and Fuzzy Logic

Trillas, Enric. author.

http://dx.doi.org/10.1007/978-3-319-56053-3

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’ in Language. The case with Fuzzy Sets -- A First Look at Conditional Statements -- Linguistic

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