Modeling Uncertainty with Fuzzy Logic

Modeling Uncertainty with Fuzzy Logic PDF Author: Asli Celikyilmaz
Publisher: Springer
ISBN: 3540899243
Category : Computers
Languages : en
Pages : 443

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Book Description
The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this afternoon? Should I take an umbrella with me? Will I be able to find parking near the campus? Should I go by bus? Such simple questions are a c- mon occurrence in our daily lives. Less simple examples: What is the probability that the price of oil will rise sharply in the near future? Should I buy Chevron stock? What are the chances that a bailout of GM, Ford and Chrysler will not s- ceed? What will be the consequences? Note that the examples in question involve both uncertainty and imprecision. In the real world, this is the norm rather than exception. There is a deep-seated tradition in science of employing probability theory, and only probability theory, to deal with uncertainty and imprecision. The mon- oly of probability theory came to an end when fuzzy logic made its debut. H- ever, this is by no means a widely accepted view. The belief persists, especially within the probability community, that probability theory is all that is needed to deal with uncertainty. To quote a prominent Bayesian, Professor Dennis Lindley, “The only satisfactory description of uncertainty is probability.

Modeling Uncertainty with Fuzzy Logic

Modeling Uncertainty with Fuzzy Logic PDF Author: Asli Celikyilmaz
Publisher: Springer
ISBN: 3540899243
Category : Computers
Languages : en
Pages : 443

Get Book Here

Book Description
The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this afternoon? Should I take an umbrella with me? Will I be able to find parking near the campus? Should I go by bus? Such simple questions are a c- mon occurrence in our daily lives. Less simple examples: What is the probability that the price of oil will rise sharply in the near future? Should I buy Chevron stock? What are the chances that a bailout of GM, Ford and Chrysler will not s- ceed? What will be the consequences? Note that the examples in question involve both uncertainty and imprecision. In the real world, this is the norm rather than exception. There is a deep-seated tradition in science of employing probability theory, and only probability theory, to deal with uncertainty and imprecision. The mon- oly of probability theory came to an end when fuzzy logic made its debut. H- ever, this is by no means a widely accepted view. The belief persists, especially within the probability community, that probability theory is all that is needed to deal with uncertainty. To quote a prominent Bayesian, Professor Dennis Lindley, “The only satisfactory description of uncertainty is probability.

Modeling Uncertainty with Fuzzy Logic

Modeling Uncertainty with Fuzzy Logic PDF Author: Asli Celikyilmaz
Publisher: Springer Science & Business Media
ISBN: 3540899235
Category : Computers
Languages : en
Pages : 443

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Book Description
The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this afternoon? Should I take an umbrella with me? Will I be able to find parking near the campus? Should I go by bus? Such simple questions are a c- mon occurrence in our daily lives. Less simple examples: What is the probability that the price of oil will rise sharply in the near future? Should I buy Chevron stock? What are the chances that a bailout of GM, Ford and Chrysler will not s- ceed? What will be the consequences? Note that the examples in question involve both uncertainty and imprecision. In the real world, this is the norm rather than exception. There is a deep-seated tradition in science of employing probability theory, and only probability theory, to deal with uncertainty and imprecision. The mon- oly of probability theory came to an end when fuzzy logic made its debut. H- ever, this is by no means a widely accepted view. The belief persists, especially within the probability community, that probability theory is all that is needed to deal with uncertainty. To quote a prominent Bayesian, Professor Dennis Lindley, “The only satisfactory description of uncertainty is probability.

Uncertainty Modeling in Vibration, Control and Fuzzy Analysis of Structural Systems

Uncertainty Modeling in Vibration, Control and Fuzzy Analysis of Structural Systems PDF Author: Bilal M. Ayyub
Publisher: World Scientific
ISBN: 9810231342
Category : Technology & Engineering
Languages : en
Pages : 382

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Book Description
This book gives an overview of the current state of uncertainty modeling in vibration, control, and fuzzy analysis of structural and mechanical systems. It is a coherent compendium written by leading experts and offers the reader a sampling of exciting research areas in several fast-growing branches in this field. Uncertainty modeling and analysis are becoming an integral part of system definition and modeling in many fields. The book consists of ten chapters that report the work of researchers, scientists and engineers on theoretical developments and diversified applications in engineering systems. They deal with modeling for vibration, control, and fuzzy analysis of structural and mechanical systems under uncertain conditions. The book designed for readers who are familiar with the fundamentals and wish to study a particular topic or use the book as an authoritative reference. It gives readers a sophisticated toolbox for tackling modeling problems in mechanical and structural systems in real-world situations. The book is part of a series on Stability, Vibration and Control of Structures, and provides vital information in these areas.

Policy Decision Modeling with Fuzzy Logic

Policy Decision Modeling with Fuzzy Logic PDF Author: Ali Guidara
Publisher: Springer Nature
ISBN: 3030626288
Category : Technology & Engineering
Languages : en
Pages : 140

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Book Description
This book introduces the concept of policy decision emergence and its dynamics at the sub systemic level of the decision process. This level constitutes the breeding ground of the emergence of policy decisions but remains unexplored due to the absence of adequate tools. It is a nonlinear complex system made of several entities that interact dynamically. The behavior of such a system cannot be understood with linear and deterministic methods. The book presents an innovative multidisciplinary approach that results in the development of a Policy Decision Emergence Simulation Model (PODESIM). This computational model is a multi-level fuzzy inference system that allows the identification of the decision emergence levers. This development represents a major advancement in the field of public policy decision studies. It paves the way for decision emergence modeling and simulation by bridging complex systems theory, multiple streams theory, and fuzzy logic theory.

Uncertain Rule-based Fuzzy Logic Systems

Uncertain Rule-based Fuzzy Logic Systems PDF Author: Jerry M. Mendel
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 584

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Book Description
Jerry Mendel explains the complete development of fuzzy logic systems and explores a new methodology to build better and more intelligent systems. Two case studies are carried throughout the book to illustrate and expand on the theories introduced.

Uncertain Rule-Based Fuzzy Systems

Uncertain Rule-Based Fuzzy Systems PDF Author: Jerry M. Mendel
Publisher: Springer
ISBN: 3319513702
Category : Technology & Engineering
Languages : en
Pages : 701

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Book Description
The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material.

Fuzzy Sets and Their Extensions: Representation, Aggregation and Models

Fuzzy Sets and Their Extensions: Representation, Aggregation and Models PDF Author: Humberto Bustince
Publisher: Springer
ISBN: 3540737235
Category : Computers
Languages : en
Pages : 674

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Book Description
This carefully edited book presents an up-to-date state of current research in the use of fuzzy sets and their extensions. It pays particular attention to foundation issues and to their application to four important areas where fuzzy sets are seen to be an important tool for modeling and solving problems. The book’s 34 chapters deal with the subject with clarity and effectiveness. They include four review papers introducing some non-standard representations

Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems

Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems PDF Author: Andras - Bardossy
Publisher: CRC Press
ISBN: 0429610866
Category : Technology & Engineering
Languages : en
Pages : 245

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Book Description
This book presents in a systematic and comprehensive manner the modeling of uncertainty, vagueness, or imprecision, alias "fuzziness," in just about any field of science and engineering. It delivers a usable methodology for modeling in the absence of real-time feedback. The book includes a short introduction to fuzzy logic containing basic definitions of fuzzy set theory and fuzzy rule systems. It describes methods for the assessment of rule systems, systems with discrete response sets, for modeling time series, for exact physical systems, examines verification and redundancy issues, and investigates rule response functions. Definitions and propositions, some of which have not been published elsewhere, are provided; numerous examples as well as references to more elaborate case studies are also given. Fuzzy rule-based modeling has the potential to revolutionize fields such as hydrology because it can handle uncertainty in modeling problems too complex to be approached by a stochastic analysis. There is also excellent potential for handling large-scale systems such as regionalization or highly non-linear problems such as unsaturated groundwater pollution.

Uncertainty Modeling

Uncertainty Modeling PDF Author: Vladik Kreinovich
Publisher: Springer
ISBN: 3319510525
Category : Technology & Engineering
Languages : en
Pages : 298

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Book Description
This book commemorates the 65th birthday of Dr. Boris Kovalerchuk, and reflects many of the research areas covered by his work. It focuses on data processing under uncertainty, especially fuzzy data processing, when uncertainty comes from the imprecision of expert opinions. The book includes 17 authoritative contributions by leading experts.

Uncertainty Management with Fuzzy and Rough Sets

Uncertainty Management with Fuzzy and Rough Sets PDF Author: Rafael Bello
Publisher: Springer
ISBN: 303010463X
Category : Technology & Engineering
Languages : en
Pages : 424

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Book Description
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.