Expert Fuzzy Information Processing
Expert Fuzzy Information Processing

Author: Olga Poleshchuk

Publisher: Springer Science & Business Media

Published: 2011-04-15

Total Pages: 248

ISBN-13: 3642201245

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This book deals with expert evaluation models in the form of semantic spaces with completeness and orthogonality properties (complete orthogonal semantic spaces). Theoretical and practical studies of some researchers have shown that these spaces describe expert evaluations most adequately, and as a result they were often included in more sophisticated models of intellectual systems for decision making and data analysis. Methods for constructing expert evaluation models of characteristics, comparative analysis of these models, studies of structural composition of their sets and constructing of generalized models are described. Models to obtain rating points for objects and groups of objects with qualitative and quantitative characteristics are presented. A number of regression models combining elements of classical and fuzzy regressions are presented. All methods and models developed by the authors and described in the book are illustrated with examples from various fields of human activities. This book meant for scientists in the field of computer science, expert systems, artificial intelligence and decision making; and also for engineers, post-graduate students and students who study the fuzzy set theory and its applications.

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations

Author: Jesús Medina

Publisher: Springer

Published: 2018-05-30

Total Pages: 835

ISBN-13: 3319914731

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This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).

Applied Fuzzy Arithmetic
Applied Fuzzy Arithmetic

Author: Michael Hanss

Publisher: Springer Science & Business Media

Published: 2005-12-27

Total Pages: 261

ISBN-13: 3540273174

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First book that provides both theory and real world applications of fuzzy arithmetic in a comprehensive style. Provides a well-structured compendium that offers both a deeper knowledge about the theory of fuzzy arithmetic and an extensive view on its applications in the engineering sciences making it useful for graduate courses, researchers and engineers. Presents the basic definitions and fundamental principles of fuzzy arithmetic, derived from fuzzy set theory. Summarizes the state-of-the-art stage of fuzzy arithmetic, offers a comprehensive composition of different approaches including their benefits and drawbacks, and finally, and presents a completely new methodology of implementation of fuzzy arithmetic with particular emphasis on its subsequent application to real-world systems. Concentrates on the application of fuzzy arithmetic to the simulation, analysis and identification of systems with uncertain model parameters, as they appear in various disciplines of engineering science. Focuses on mechanical engineering, geotechnical engineering, biomedical engineering, and control engineering.

Computing Statistics under Interval and Fuzzy Uncertainty
Computing Statistics under Interval and Fuzzy Uncertainty

Author: Hung T. Nguyen

Publisher: Springer

Published: 2011-11-17

Total Pages: 412

ISBN-13: 3642249051

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In many practical situations, we are interested in statistics characterizing a population of objects: e.g. in the mean height of people from a certain area. Most algorithms for estimating such statistics assume that the sample values are exact. In practice, sample values come from measurements, and measurements are never absolutely accurate. Sometimes, we know the exact probability distribution of the measurement inaccuracy, but often, we only know the upper bound on this inaccuracy. In this case, we have interval uncertainty: e.g. if the measured value is 1.0, and inaccuracy is bounded by 0.1, then the actual (unknown) value of the quantity can be anywhere between 1.0 - 0.1 = 0.9 and 1.0 + 0.1 = 1.1. In other cases, the values are expert estimates, and we only have fuzzy information about the estimation inaccuracy. This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to information technology (maintaining privacy), to computer engineering (design of computer chips), and to data processing in geosciences, radar imaging, and structural mechanics.

18th International Conference of the North American Fuzzy Information Processing Society--NAFIPS
18th International Conference of the North American Fuzzy Information Processing Society--NAFIPS

Author: North American Fuzzy Information Processing Society. Conference

Publisher: Institute of Electrical & Electronics Engineers(IEEE)

Published: 1999

Total Pages: 926

ISBN-13: 9780780352117

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The theme of NAFIPS'99 is "Real World Applications of Fuzzy Logic and Soft Computing." The focus of the conference will be on applications of fuzzy logic and soft computing for challenging, real-world problems.

Information Processing and Management of Uncertainty in Knowledge-Based Systems
Information Processing and Management of Uncertainty in Knowledge-Based Systems

Author: Marie-Jeanne Lesot

Publisher: Springer Nature

Published: 2020-06-05

Total Pages: 779

ISBN-13: 3030501469

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This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually. The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I; machine learning II; XAI; image processing; temporal data processing; text analysis and processing; fuzzy interval analysis; theoretical and applied aspects of imprecise probabilities; similarities in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery; computational intelligence for logistics and transportation problems; fuzzy implication functions; soft methods in statistics and data analysis; image understanding and explainable AI; fuzzy and generalized quantifier theory; mathematical methods towards dealing with uncertainty in applied sciences; statistical image processing and analysis, with applications in neuroimaging; interval uncertainty; discrete models and computational intelligence; current techniques to model, process and describe time series; mathematical fuzzy logic and graded reasoning models; formal concept analysis, rough sets, general operators and related topics; computational intelligence methods in information modelling, representation and processing.

Author:

Publisher: IOS Press

Published:

Total Pages: 7289

ISBN-13:

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Readings in Fuzzy Sets for Intelligent Systems
Readings in Fuzzy Sets for Intelligent Systems

Author: Didier J. Dubois

Publisher: Morgan Kaufmann

Published: 2014-05-12

Total Pages: 929

ISBN-13: 1483214508

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Readings in Fuzzy Sets for Intelligent Systems is a collection of readings that explore the main facets of fuzzy sets and possibility theory and their use in intelligent systems. Basic notions in fuzzy set theory are discussed, along with fuzzy control and approximate reasoning. Uncertainty and informativeness, information processing, and membership, cognition, neural networks, and learning are also considered. Comprised of eight chapters, this book begins with a historical background on fuzzy sets and possibility theory, citing some forerunners who discussed ideas or formal definitions very close to the basic notions introduced by Lotfi Zadeh (1978). The reader is then introduced to fundamental concepts in fuzzy set theory, including symmetric summation and the setting of fuzzy logic; uncertainty and informativeness; and fuzzy control. Subsequent chapters deal with approximate reasoning; information processing; decision and management sciences; and membership, cognition, neural networks, and learning. Numerical methods for fuzzy clustering are described, and adaptive inference in fuzzy knowledge networks is analyzed. This monograph will be of interest to both students and practitioners in the fields of computer science, information science, applied mathematics, and artificial intelligence.