Uncertainty Based Information

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Uncertainty-Based Information

Author : George Klir
Publisher : Springer Science & Business Media
ISBN 13 : 9783790812428
Total Pages : 196 pages
Book Rating : 4.8/5 (124 download)

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Book Synopsis Uncertainty-Based Information by : George Klir

Download or read book Uncertainty-Based Information written by George Klir and published by Springer Science & Business Media. This book was released on 1999-10-19 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information is precious. It reduces our uncertainty in making decisions. Knowledge about the outcome of an uncertain event gives the possessor an advantage. It changes the course of lives, nations, and history itself. Information is the food of Maxwell's demon. His power comes from know ing which particles are hot and which particles are cold. His existence was paradoxical to classical physics and only the realization that information too was a source of power led to his taming. Information has recently become a commodity, traded and sold like or ange juice or hog bellies. Colleges give degrees in information science and information management. Technology of the computer age has provided access to information in overwhelming quantity. Information has become something worth studying in its own right. The purpose of this volume is to introduce key developments and results in the area of generalized information theory, a theory that deals with uncertainty-based information within mathematical frameworks that are broader than classical set theory and probability theory. The volume is organized as follows.

Uncertainty-Based Information

Author : George J. Klir
Publisher : Physica
ISBN 13 : 3790818690
Total Pages : 180 pages
Book Rating : 4.7/5 (98 download)

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Book Synopsis Uncertainty-Based Information by : George J. Klir

Download or read book Uncertainty-Based Information written by George J. Klir and published by Physica. This book was released on 2013-06-05 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information is precious. It reduces our uncertainty in making decisions. Knowledge about the outcome of an uncertain event gives the possessor an advantage. It changes the course of lives, nations, and history itself. Information is the food of Maxwell's demon. His power comes from know ing which particles are hot and which particles are cold. His existence was paradoxical to classical physics and only the realization that information too was a source of power led to his taming. Information has recently become a commodity, traded and sold like or ange juice or hog bellies. Colleges give degrees in information science and information management. Technology of the computer age has provided access to information in overwhelming quantity. Information has become something worth studying in its own right. The purpose of this volume is to introduce key developments and results in the area of generalized information theory, a theory that deals with uncertainty-based information within mathematical frameworks that are broader than classical set theory and probability theory. The volume is organized as follows.

Uncertainty and Information

Author : George J. Klir
Publisher : John Wiley & Sons
ISBN 13 : 0471755567
Total Pages : 499 pages
Book Rating : 4.4/5 (717 download)

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Book Synopsis Uncertainty and Information by : George J. Klir

Download or read book Uncertainty and Information written by George J. Klir and published by John Wiley & Sons. This book was released on 2005-11-22 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deal with information and uncertainty properly and efficientlyusing tools emerging from generalized information theory Uncertainty and Information: Foundations of Generalized InformationTheory contains comprehensive and up-to-date coverage of resultsthat have emerged from a research program begun by the author inthe early 1990s under the name "generalized information theory"(GIT). This ongoing research program aims to develop a formalmathematical treatment of the interrelated concepts of uncertaintyand information in all their varieties. In GIT, as in classicalinformation theory, uncertainty (predictive, retrodictive,diagnostic, prescriptive, and the like) is viewed as amanifestation of information deficiency, while information isviewed as anything capable of reducing the uncertainty. A broadconceptual framework for GIT is obtained by expanding theformalized language of classical set theory to include moreexpressive formalized languages based on fuzzy sets of varioustypes, and by expanding classical theory of additive measures toinclude more expressive non-additive measures of varioustypes. This landmark book examines each of several theories for dealingwith particular types of uncertainty at the following fourlevels: * Mathematical formalization of the conceived type ofuncertainty * Calculus for manipulating this particular type ofuncertainty * Justifiable ways of measuring the amount of uncertainty in anysituation formalizable in the theory * Methodological aspects of the theory With extensive use of examples and illustrations to clarify complexmaterial and demonstrate practical applications, generoushistorical and bibliographical notes, end-of-chapter exercises totest readers' newfound knowledge, glossaries, and an Instructor'sManual, this is an excellent graduate-level textbook, as well as anoutstanding reference for researchers and practitioners who dealwith the various problems involving uncertainty and information. AnInstructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.

Uncertainty and Vagueness in Knowledge Based Systems

Author : Rudolf Kruse
Publisher : Springer Science & Business Media
ISBN 13 : 3642767028
Total Pages : 495 pages
Book Rating : 4.6/5 (427 download)

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Book Synopsis Uncertainty and Vagueness in Knowledge Based Systems by : Rudolf Kruse

Download or read book Uncertainty and Vagueness in Knowledge Based Systems written by Rudolf Kruse and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary aim of this monograph is to provide a formal framework for the representation and management of uncertainty and vagueness in the field of artificial intelligence. It puts particular emphasis on a thorough analysis of these phenomena and on the development of sound mathematical modeling approaches. Beyond this theoretical basis the scope of the book includes also implementational aspects and a valuation of existing models and systems. The fundamental ambition of this book is to show that vagueness and un certainty can be handled adequately by using measure-theoretic methods. The presentation of applicable knowledge representation formalisms and reasoning algorithms substantiates the claim that efficiency requirements do not necessar ily require renunciation of an uncompromising mathematical modeling. These results are used to evaluate systems based on probabilistic methods as well as on non-standard concepts such as certainty factors, fuzzy sets or belief functions. The book is intended to be self-contained and addresses researchers and practioneers in the field of knowledge based systems. It is in particular suit able as a textbook for graduate-level students in AI, operations research and applied probability. A solid mathematical background is necessary for reading this book. Essential parts of the material have been the subject of courses given by the first author for students of computer science and mathematics held since 1984 at the University in Braunschweig.

Information Processing and Management of Uncertainty in Knowledge-Based Systems

Author : Marie-Jeanne Lesot
Publisher : Springer Nature
ISBN 13 : 3030501469
Total Pages : 779 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Information Processing and Management of Uncertainty in Knowledge-Based Systems by : Marie-Jeanne Lesot

Download or read book Information Processing and Management of Uncertainty in Knowledge-Based Systems written by Marie-Jeanne Lesot and published by Springer Nature. This book was released on 2020-06-05 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

A Methodology for Uncertainty in Knowledge-Based Systems

Author : Kurt Weichselberger
Publisher : Lecture Notes in Artificial Intelligence
ISBN 13 :
Total Pages : 154 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis A Methodology for Uncertainty in Knowledge-Based Systems by : Kurt Weichselberger

Download or read book A Methodology for Uncertainty in Knowledge-Based Systems written by Kurt Weichselberger and published by Lecture Notes in Artificial Intelligence. This book was released on 1990-03-07 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book the consequent use of probability theory is proposed for handling uncertainty in expert systems. It is shown that methods violating this suggestion may have dangerous consequences (e.g., the Dempster-Shafer rule and the method used in MYCIN). The necessity of some requirements for a correct combining of uncertain information in expert systems is demonstrated and suitable rules are provided. The possibility is taken into account that interval estimates are given instead of exact information about probabilities. For combining information containing interval estimates rules are provided which are useful in many cases.

Uncertainty Management in Information Systems

Author : Amihai Motro
Publisher : Springer Science & Business Media
ISBN 13 : 1461562457
Total Pages : 473 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Uncertainty Management in Information Systems by : Amihai Motro

Download or read book Uncertainty Management in Information Systems written by Amihai Motro and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: As its title suggests, "Uncertainty Management in Information Systems" is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information systems require stronger capabilities in the area of uncertainty management. Our hope is that lasting interaction between these two areas would facilitate a new generation of information systems that will be capable of servicing these applications. Although there are researchers in information systems who have addressed themselves to issues of uncertainty, as well as researchers in uncertainty modeling who have considered the pragmatic demands and constraints of information systems, to a large extent there has been only limited interaction between these two areas. As the subtitle, "From Needs to Solutions," indicates, this book presents view points of information systems experts on the needs that challenge the uncer tainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs.

Design Decisions Under Uncertainty with Limited Information

Author : Efstratios Nikolaidis
Publisher : CRC Press
ISBN 13 : 9781138115095
Total Pages : 538 pages
Book Rating : 4.1/5 (15 download)

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Book Synopsis Design Decisions Under Uncertainty with Limited Information by : Efstratios Nikolaidis

Download or read book Design Decisions Under Uncertainty with Limited Information written by Efstratios Nikolaidis and published by CRC Press. This book was released on 2017-06-16 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today's business environment involves design decisions with significant uncertainty. To succeed, decision-makers should replace deterministic methods with a risk-based approach that accounts for the decision maker¿s risk tolerance. In many problems, it is impractical to collect data because rare or one-time events are involved. Therefore, we need a methodology to model uncertainty and make choices when we have limited information. This methodology must use all available information and rely only on assumptions that are supported by evidence. This book explains theories and tools to represent uncertainty using both data and expert judgment. It teaches the reader how to make design or business decisions when there is limited information with these tools. Readers will learn a structured, risk-based approach, which is based on common sense principles, for design and business decisions. These decisions are consistent with the decision-maker¿s risk attitude. The book is exceptionally suited as educational material because it uses everyday language and real-life examples to elucidate concepts. It demonstrates how these concepts touch our lives through many practical examples, questions and exercises. These are designed to help students learn that first they should understand a problem and then establish a strategy for solving it, instead of using trial-and-error approaches. This volume is intended for undergraduate and graduate courses in mechanical, civil, industrial, aerospace, and ocean engineering and for researchers and professionals in these disciplines. It will also benefit managers and students in business administration who want to make good decisions with limited information.

The Analytics of Uncertainty and Information

Author : Sushil Bikhchandani
Publisher : Cambridge University Press
ISBN 13 : 1107433762
Total Pages : 509 pages
Book Rating : 4.1/5 (74 download)

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Book Synopsis The Analytics of Uncertainty and Information by : Sushil Bikhchandani

Download or read book The Analytics of Uncertainty and Information written by Sushil Bikhchandani and published by Cambridge University Press. This book was released on 2013-08-12 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been explosive progress in the economic theory of uncertainty and information in the past few decades. This subject is now taught not only in departments of economics but also in professional schools and programs oriented toward business, government and administration, and public policy. This book attempts to unify the subject matter in a simple, accessible manner. Part I of the book focuses on the economics of uncertainty; Part II examines the economics of information. This revised and updated second edition places a greater focus on game theory. New topics include posted-price markets, mechanism design, common-value auctions, and the one-shot deviation principle for repeated games.

Uncertainty Theory

Author : Baoding Liu
Publisher : Springer
ISBN 13 : 3540731652
Total Pages : 263 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Uncertainty Theory by : Baoding Liu

Download or read book Uncertainty Theory written by Baoding Liu and published by Springer. This book was released on 2007-09-14 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The purpose is to equip the readers with an axiomatic approach to deal with uncertainty. For this new edition the entire text has been totally rewritten. The chapters on chance theory and uncertainty theory are completely new. Mathematicians, researchers, engineers, designers, and students will find this work a stimulating and useful reference.

Decision Making Under Uncertainty

Author : Mykel J. Kochenderfer
Publisher : MIT Press
ISBN 13 : 0262331713
Total Pages : 350 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer

Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Reasoning about Uncertainty, second edition

Author : Joseph Y. Halpern
Publisher : MIT Press
ISBN 13 : 0262533804
Total Pages : 505 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Reasoning about Uncertainty, second edition by : Joseph Y. Halpern

Download or read book Reasoning about Uncertainty, second edition written by Joseph Y. Halpern and published by MIT Press. This book was released on 2017-04-07 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.

Uncertainty in Engineering

Author : Louis J. M. Aslett
Publisher : Springer Nature
ISBN 13 : 3030836401
Total Pages : 148 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Uncertainty in Engineering by : Louis J. M. Aslett

Download or read book Uncertainty in Engineering written by Louis J. M. Aslett and published by Springer Nature. This book was released on 2022 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners.

Uncertainty Quantification and Predictive Computational Science

Author : Ryan G. McClarren
Publisher : Springer
ISBN 13 : 3319995251
Total Pages : 349 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Uncertainty Quantification and Predictive Computational Science by : Ryan G. McClarren

Download or read book Uncertainty Quantification and Predictive Computational Science written by Ryan G. McClarren and published by Springer. This book was released on 2018-11-23 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.

Uncertainty Theory

Author : Baoding Liu
Publisher : Springer Science & Business Media
ISBN 13 : 3642139582
Total Pages : 350 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Uncertainty Theory by : Baoding Liu

Download or read book Uncertainty Theory written by Baoding Liu and published by Springer Science & Business Media. This book was released on 2011-11-07 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty theory is a branch of mathematics based on normality, monotonicity, self-duality, countable subadditivity, and product measure axioms. Uncertainty is any concept that satisfies the axioms of uncertainty theory. Thus uncertainty is neither randomness nor fuzziness. It is also known from some surveys that a lot of phenomena do behave like uncertainty. How do we model uncertainty? How do we use uncertainty theory? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory, including uncertain programming, uncertain risk analysis, uncertain reliability analysis, uncertain process, uncertain calculus, uncertain differential equation, uncertain logic, uncertain entailment, and uncertain inference. Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, system science, industrial engineering, computer science, artificial intelligence, finance, control, and management science will find this work a stimulating and useful reference.

Fuzzy Logic for the Management of Uncertainty

Author : Lotfi Asker Zadeh
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 696 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Fuzzy Logic for the Management of Uncertainty by : Lotfi Asker Zadeh

Download or read book Fuzzy Logic for the Management of Uncertainty written by Lotfi Asker Zadeh and published by Wiley-Interscience. This book was released on 1992-07-30 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy Logic for the Management of Uncertainty covers many important topics, including:" "Developments in mathematics that have paved the road for fuzzy logic;" "Deep, and of a broad perspective, exposition of virtually all approaches used in contemporary science for the representation and handling of imperfect (uncertain, imprecise, vague, ambiguous, etc.) information;" "Coverage of practically all relevant and promising directions and approaches in fuzzy logic research including LT--fuzzy logic, model theoretic approaches, intuitionistic fuzzy logic, nonmonotonic fuzzy logic, modifier fuzzy logic;" "VLSI fuzzy logic-based chips that have triggered the implementation of fuzzy logic in so many fields of science and technology;" "A broad coverage of fuzzy logic in approximate reasoning, including basic issues related to the role of fuzzy logic for approximate reasoning, analyses of various definitions of fuzzy implication that is a crucial element in fuzzy logic-based reasoning schemes,

Uncertainty Management with Fuzzy and Rough Sets

Author : Rafael Bello
Publisher : Springer
ISBN 13 : 303010463X
Total Pages : 424 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Uncertainty Management with Fuzzy and Rough Sets by : Rafael Bello

Download or read book Uncertainty Management with Fuzzy and Rough Sets written by Rafael Bello and published by Springer. This book was released on 2019-01-22 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a timely overview of fuzzy and rough set theories and methods. Based on selected contributions presented at the International Symposium on Fuzzy and Rough Sets, ISFUROS 2017, held in Varadero, Cuba, on October 24-26, 2017, the book also covers related approaches, such as hybrid rough-fuzzy sets and hybrid fuzzy-rough sets and granular computing, as well as a number of applications, from big data analytics, to business intelligence, security, robotics, logistics, wireless sensor networks and many more. It is intended as a source of inspiration for PhD students and researchers in the field, fostering not only new ideas but also collaboration between young researchers and institutions and established ones.