Fuzzy Statistical Decision Making

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Fuzzy Statistical Decision-Making

Author : Cengiz Kahraman,Özgür Kabak
Publisher : Springer
Page : 356 pages
File Size : 46,7 Mb
Release : 2016-07-15
Category : Technology & Engineering
ISBN : 9783319390147

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Fuzzy Statistical Decision-Making by Cengiz Kahraman,Özgür Kabak Pdf

This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.

Fuzzy Theories on Decision Making

Author : Walter J.M. Kickert
Publisher : Springer Science & Business Media
Page : 202 pages
File Size : 41,8 Mb
Release : 1979-01-31
Category : Business & Economics
ISBN : 9020707604

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Fuzzy Theories on Decision Making by Walter J.M. Kickert Pdf

Type-2 Fuzzy Decision-Making Theories, Methodologies and Applications

Author : Jindong Qin,Xinwang Liu
Publisher : Springer
Page : 271 pages
File Size : 41,8 Mb
Release : 2019-08-09
Category : Mathematics
ISBN : 9789811398919

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Type-2 Fuzzy Decision-Making Theories, Methodologies and Applications by Jindong Qin,Xinwang Liu Pdf

This book integrates the type-2 fuzzy sets and multiple criteria decision making analysis in recent years and offers an authoritative treatise on the essential topics, both at the theoretical and applied end. In this book, some basic theory, type-2 fuzzy sets, methodology, algorithms, are introduced and then some compelling case studies in decision problems are covered in depth. The authors offer an authoritative treatise on the essential topics, both at the theoretical and applied end; In a systematic and logically organized way, the book exposes the reader to the essentials of the theory of type-2 fuzzy sets, methodology, algorithms, and their applications. Numerous techniques of decision making are carefully generalized by bringing the ideas of type-2 fuzzy sets; this concerns well-known methods including TOPSIS, Analytical Network Process, TODIM, and VIKOR. This book exposes the readers to the essentials of the theory of type-2 fuzzy sets, methodology, algorithms, and their applications.

Fuzzy Sets in Decision Analysis, Operations Research and Statistics

Author : Roman Slowiński
Publisher : Springer Science & Business Media
Page : 467 pages
File Size : 52,5 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461556459

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Fuzzy Sets in Decision Analysis, Operations Research and Statistics by Roman Slowiński Pdf

Fuzzy Sets in Decision Analysis, Operations Research and Statistics includes chapters on fuzzy preference modeling, multiple criteria analysis, ranking and sorting methods, group decision-making and fuzzy game theory. It also presents optimization techniques such as fuzzy linear and non-linear programming, applications to graph problems and fuzzy combinatorial methods such as fuzzy dynamic programming. In addition, the book also accounts for advances in fuzzy data analysis, fuzzy statistics, and applications to reliability analysis. These topics are covered within four parts: Decision Making, Mathematical Programming, Statistics and Data Analysis, and Reliability, Maintenance and Replacement. The scope and content of the book has resulted from multiple interactions between the editor of the volume, the series editors, the series advisory board, and experts in each chapter area. Each chapter was written by a well-known researcher on the topic and reviewed by other experts in the area. These expert reviewers sometimes became co-authors because of the extent of their contribution to the chapter. As a result, twenty-five authors from twelve countries and four continents were involved in the creation of the 13 chapters, which enhances the international character of the project and gives an idea of how carefully the Handbook has been developed.

Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making

Author : Mario Fedrizzi
Publisher : Springer Science & Business Media
Page : 410 pages
File Size : 42,6 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9783642466441

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Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making by Mario Fedrizzi Pdf

In the literature of decision analysis it is traditional to rely on the tools provided by probability theory to deal with problems in which uncertainty plays a substantive role. In recent years, however, it has become increasingly clear that uncertainty is a mul tifaceted concept in which some of the important facets do not lend themselves to analysis by probability-based methods. One such facet is that of fuzzy imprecision, which is associated with the use of fuzzy predicates exemplified by small, large, fast, near, likely, etc. To be more specific, consider a proposition such as "It is very unlikely that the price of oil will decline sharply in the near future," in which the italicized words play the role of fuzzy predicates. The question is: How can one express the mean ing of this proposition through the use of probability-based methods? If this cannot be done effectively in a probabilistic framework, then how can one employ the information provided by the proposition in question to bear on a decision relating to an investment in a company engaged in exploration and marketing of oil? As another example, consider a collection of rules of the form "If X is Ai then Y is B,," j = 1, . . . , n, in which X and Yare real-valued variables and Ai and Bi are fuzzy numbers exemplified by small, large, not very small, close to 5, etc.

Fuzzy Sets and Fuzzy Decision-Making

Author : Hongxing Li,Vincent C. Yen
Publisher : CRC Press
Page : 288 pages
File Size : 49,8 Mb
Release : 1995-07-03
Category : Computers
ISBN : 0849389313

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Fuzzy Sets and Fuzzy Decision-Making by Hongxing Li,Vincent C. Yen Pdf

The increasing number of applications of fuzzy mathematics has generated interest in widely ranging fields, from engineering and medicine to the humanities and management sciences. Fuzzy Sets and Fuzzy Decision-Making provides an introduction to fuzzy set theory and lays the foundation of fuzzy mathematics and its applications to decision-making. New concepts are simplified with the use of figures and diagrams, and methods are discussed in terms of their direct applications in obtaining solutions to real problems, particularly to decision-related problems. The first chapter presents the current state of knowledge of fuzzy set theory, using pan-Venn-diagrams to illustrate mathematical concepts. The second chapter clearly describes the theory of factor spaces, on which fuzzy decision-making is based. The remainder of the book is devoted to the methods, applications, techniques, and examples of this fuzzy decision-making, and includes methods for determining membership functions and for treating multifactorial and variable weights analyses.

Fuzzy Multi-Criteria Decision Making

Author : Cengiz Kahraman
Publisher : Springer Science & Business Media
Page : 591 pages
File Size : 44,9 Mb
Release : 2008-08-09
Category : Computers
ISBN : 9780387768137

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Fuzzy Multi-Criteria Decision Making by Cengiz Kahraman Pdf

This work examines all the fuzzy multicriteria methods recently developed, such as fuzzy AHP, fuzzy TOPSIS, interactive fuzzy multiobjective stochastic linear programming, fuzzy multiobjective dynamic programming, grey fuzzy multiobjective optimization, fuzzy multiobjective geometric programming, and more. Each of the 22 chapters includes practical applications along with new developments/results. This book may be used as a textbook in graduate operations research, industrial engineering, and economics courses. It will also be an excellent resource, providing new suggestions and directions for further research, for computer programmers, mathematicians, and scientists in a variety of disciplines where multicriteria decision making is needed.

The Signed Distance Measure in Fuzzy Statistical Analysis

Author : Rédina Berkachy
Publisher : Springer Nature
Page : 356 pages
File Size : 55,5 Mb
Release : 2021-10-31
Category : Computers
ISBN : 9783030769161

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The Signed Distance Measure in Fuzzy Statistical Analysis by Rédina Berkachy Pdf

The main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy. The proposed method for estimating these intervals is based on the likelihood method and employs the bootstrap technique. A new metric generalizing the signed distance measure is also developed. In turn, the book presents two conceptually diverse applications in which defended intervals play a role: one is a novel methodology for evaluating linguistic questionnaires developed at the global and individual levels; the other is an extension of the multi-ways analysis of variance to the space of fuzzy sets. To illustrate these approaches, the book presents several empirical and simulation-based studies with synthetic and real data sets. In closing, it presents a coherent R package called “FuzzySTs” which covers all the previously mentioned concepts with full documentation and selected use cases. Given its scope, the book will be of interest to all researchers whose work involves advanced fuzzy statistical methods.

Multiperson Decision Making Models Using Fuzzy Sets and Possibility Theory

Author : J. Kacprzyk,Mario Fedrizzi
Publisher : Springer Science & Business Media
Page : 349 pages
File Size : 52,6 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9789400921092

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Multiperson Decision Making Models Using Fuzzy Sets and Possibility Theory by J. Kacprzyk,Mario Fedrizzi Pdf

Decision making is certainly a very crucial component of many human activities. It is, therefore, not surprising that models of decisions play a very important role not only in decision theory but also in areas such as operations Research, Management science, social Psychology etc . . The basic model of a decision in classical normative decision theory has very little in common with real decision making: It portrays a decision as a clear-cut act of choice, performed by one individual decision maker and in which states of nature, possible actions, results and preferences are well and crisply defined. The only compo nent in which uncertainty is permitted is the occurence of the different states of nature, for which probabilistic descriptions are allowed. These probabilities are generally assumed to be known numerically, i. e. as single probabili ties or as probability distribution functions. Extensions of this basic model can primarily be conceived in three directions: 1. Rather than a single decision maker there are several decision makers involved. This has lead to the areas of game theory, team theory and group decision theory. 2. The preference or utility function is not single valued but rather vector valued. This extension is considered in multiattribute utility theory and in multicritieria analysis. 3.

Fuzzy Statistical Inferences Based on Fuzzy Random Variables

Author : Gholamreza Hesamian
Publisher : CRC Press
Page : 452 pages
File Size : 40,5 Mb
Release : 2022-02-24
Category : Mathematics
ISBN : 9781000539820

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Fuzzy Statistical Inferences Based on Fuzzy Random Variables by Gholamreza Hesamian Pdf

This book presents the most commonly used techniques for the most statistical inferences based on fuzzy data. It brings together many of the main ideas used in statistical inferences in one place, based on fuzzy information including fuzzy data. This book covers a much wider range of topics than a typical introductory text on fuzzy statistics. It includes common topics like elementary probability, descriptive statistics, hypothesis tests, one-way ANOVA, control-charts, reliability systems and regression models. The reader is assumed to know calculus and a little fuzzy set theory. The conventional knowledge of probability and statistics is required. Key Features: Includes example in Mathematica and MATLAB. Contains theoretical and applied exercises for each section. Presents various popular methods for analyzing fuzzy data. The book is suitable for students and researchers in statistics, social science, engineering, and economics, and it can be used at graduate and P.h.D level.

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making

Author : Cengiz Kahraman,Selcuk Cebi,Sezi Cevik Onar,Basar Oztaysi,A. Cagri Tolga,Irem Ucal Sari
Publisher : Springer
Page : 1392 pages
File Size : 44,7 Mb
Release : 2019-07-05
Category : Technology & Engineering
ISBN : 9783030237561

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Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making by Cengiz Kahraman,Selcuk Cebi,Sezi Cevik Onar,Basar Oztaysi,A. Cagri Tolga,Irem Ucal Sari Pdf

This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.

Fuzzy Multiple Attribute Decision Making

Author : Shu-Jen Chen,Ching-Lai Hwang
Publisher : Springer Science & Business Media
Page : 552 pages
File Size : 43,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9783642467684

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Fuzzy Multiple Attribute Decision Making by Shu-Jen Chen,Ching-Lai Hwang Pdf

This monograph is intended for an advanced undergraduate or graduate course as well as for researchers, who want a compilation of developments in this rapidly growing field of operations research. This is a sequel to our previous works: "Multiple Objective Decision Making--Methods and Applications: A state-of-the-Art Survey" (No.164 of the Lecture Notes); "Multiple Attribute Decision Making--Methods and Applications: A State-of-the-Art Survey" (No.186 of the Lecture Notes); and "Group Decision Making under Multiple Criteria--Methods and Applications" (No.281 of the Lecture Notes). In this monograph, the literature on methods of fuzzy Multiple Attribute Decision Making (MADM) has been reviewed thoroughly and critically, and classified systematically. This study provides readers with a capsule look into the existing methods, their characteristics, and applicability to the analysis of fuzzy MADM problems. The basic concepts and algorithms from the classical MADM methods have been used in the development of the fuzzy MADM methods. We give an overview of the classical MADM in Chapter II. Chapter III presents the basic concepts and mathematical operations of fuzzy set theory with simple numerical examples in a easy-to-read and easy-to-follow manner. Fuzzy MADM methods basically consist of two phases: (1) the aggregation of the performance scores with respect to all the attributes for each alternative, and (2) the rank ordering of the alternatives according to the aggregated scores.

Applied Fuzzy Systems

Author : Toshiro Terano,Kiyoji Asai,Michio Sugeno
Publisher : Academic Press
Page : 315 pages
File Size : 43,6 Mb
Release : 2014-05-10
Category : Technology & Engineering
ISBN : 9781483262932

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Applied Fuzzy Systems by Toshiro Terano,Kiyoji Asai,Michio Sugeno Pdf

Applied Fuzzy Systems provides information pertinent to the fundamental aspects of fuzzy systems theory and its application. This book discusses the development of high-level artificial intelligence and information processing systems, as well as the realization of fuzzy computers. Organized into six chapters, this book begins with an overview of the fundamental problems addressed by fuzzy systems. This text then reviews standard computer logic or two-valued Boolean algebra. Other chapters consider bus scheduling, evaluation of structural reliability, applications of schema systems for decision-making, and processing of natural-language information and systems for medical diagnosis as examples of fuzzy expert systems. This book discusses as well a practical fuzzy expert system for durability evaluations of reinforced concrete slabs for bridges, along with an example of application. The final chapter deals with the important parts of the construction of fuzzy computers, their architecture, and the outlook for the future. This book is a valuable resource for engineers, mathematicians, technicians, and research workers.

Fuzzy Decision Procedures with Binary Relations

Author : Leonid Kitainik
Publisher : Springer Science & Business Media
Page : 272 pages
File Size : 42,5 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9789401119603

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Fuzzy Decision Procedures with Binary Relations by Leonid Kitainik Pdf

In decision theory there are basically two appr~hes to the modeling of individual choice: one is based on an absolute representation of preferences leading to a ntDnerical expression of preference intensity. This is utility theory. Another approach is based on binary relations that encode pairwise preference. While the former has mainly blossomed in the Anglo-Saxon academic world, the latter is mostly advocated in continental Europe, including Russia. The advantage of the utility theory approach is that it integrates uncertainty about the state of nature, that may affect the consequences of decision. Then, the problems of choice and ranking from the knowledge of preferences become trivial once the utility function is known. In the case of the relational approach, the model does not explicitly accounts for uncertainty, hence it looks less sophisticated. On the other hand it is more descriptive than normative in the first stand because it takes the pairwise preference pattern expressed by the decision-maker as it is and tries to make the best out of it. Especially the preference relation is not supposed to have any property. The main problem with the utility theory approach is the gap between what decision-makers are and can express, and what the theory would like them to be and to be capable of expressing. With the relational approach this gap does not exist, but the main difficulty is now to build up convincing choice rules and ranking rules that may help the decision process.

Fuzzy Systems for Management

Author : Kiyoji Asai
Publisher : IOS Press
Page : 208 pages
File Size : 40,6 Mb
Release : 1995
Category : Science
ISBN : 4274900339

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Fuzzy Systems for Management by Kiyoji Asai Pdf

Management organizations for companies and government must respond in a prompt and flexible manner to the large variety of frequently changing requirements of the modern market and society. Earlier management science methods like operations research and mathematical programming often took the approach of expressing problems in equations and solving them, but these tend to lack variety and flexibility and have taken form in which human beings supplement them. Various methods have been developed to systematize the parts that depend on human beings, improve the use of computers and make it possible for managers with little experience to use them. The fuzzy theory focuses on the general situation and generalization of the intelligent information processing of human beings and attempts to create models that simulate these.