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Fuzzy Sets, Decision Making, and Expert Systems

Author: Hans-Jürgen Zimmermann

Publisher: Springer Science & Business Media

ISBN:

Category: Business & Economics

Page: 336

View: 106

In the two decades since its inception by L. Zadeh, the theory of fuzzy sets has matured into a wide-ranging collection of concepts, models, and tech niques for dealing with complex phenomena which do not lend themselves to analysis by classical methods based on probability theory and bivalent logic. Nevertheless, a question which is frequently raised by the skeptics is: Are there, in fact, any significant problem areas in which the use of the theory of fuzzy sets leads to results which could not be obtained by classical methods? The approximately 5000 publications in this area, which are scattered over many areas such as artificial intelligence, computer science, control engineering, decision making, logic, operations research, pattern recognition, robotics and others, provide an affirmative answer to this question. In spite of the large number of publications, good and comprehensive textbooks which could facilitate the access of newcomers to this area and support teaching were missing until recently. To help to close this gap and to provide a textbook for courses in fuzzy set theory which can also be used as an introduction to this field, the first volume ofthis book was published in 1985 [Zimmermann 1985 b]. This volume tried to cover fuzzy set theory and its applications as extensively as possible. Applications could, therefore, only be described to a limited extent and not very detailed.

Fuzzy Sets, Decision Making, and Expert Systems

Author: H. J. Zimmermann

Publisher:

ISBN:

Category: Expert systems (Computer science)

Page: 335

View: 437

Fuzzy Set Theory—and Its Applications

Author: Hans-Jürgen Zimmermann

Publisher: Springer Science & Business Media

ISBN:

Category: Mathematics

Page: 514

View: 939

This introduction to fuzzy set theory and its multitude of applications seeks to balance the character of the book with the dynamic nature of the research. This edition includes new chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. Existing material has been updated, and extended exercises are included.

Fuzzy Reasoning in Decision Making and Optimization

Author: Christer Carlsson

Publisher: Physica

ISBN:

Category: Business & Economics

Page: 338

View: 407

Many decision-making tasks are too complex to be understood quantitatively, however, humans succeed by using knowledge that is imprecise rather than precise. Fuzzy logic resembles human reasoning in its use of imprecise informa tion to generate decisions. Unlike classical logic which requires a deep under standing of a system, exact equations, and precise numeric values, fuzzy logic incorporates an alternative way of thinking, which allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience. Fuzzy logic allows expressing this knowledge with subjective concepts such as very big and a long time which are mapped into exact numeric ranges. Since knowledge can be expressed in a more natural by using fuzzy sets, many decision (and engineering) problems can be greatly simplified. Fuzzy logic provides an inference morphology that enables approximate human reasoning capabilities to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength to capture the un certainties associated with human cognitive processes, such as thinking and reasoning. The conventional approaches to knowledge representation lack the means for representating the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic do not provide an appropriate con ceptual framework for dealing with the representation of commonsense knowl edge, since such knowledge is by its nature both lexically imprecise and non categorical.

Introduction to Neuro-Fuzzy Systems

Author: Robert Fuller

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 289

View: 794

Fuzzy sets were introduced by Zadeh (1965) as a means of representing and manipulating data that was not precise, but rather fuzzy. Fuzzy logic pro vides an inference morphology that enables approximate human reasoning capabilities to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength to capture the uncertainties associ ated with human cognitive processes, such as thinking and reasoning. The conventional approaches to knowledge representation lack the means for rep resentating the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic and classical probablity theory do not provide an appropriate conceptual framework for dealing with the representation of com monsense knowledge, since such knowledge is by its nature both lexically imprecise and noncategorical. The developement of fuzzy logic was motivated in large measure by the need for a conceptual framework which can address the issue of uncertainty and lexical imprecision. Some of the essential characteristics of fuzzy logic relate to the following [242]. • In fuzzy logic, exact reasoning is viewed as a limiting case of ap proximate reasoning. • In fuzzy logic, everything is a matter of degree. • In fuzzy logic, knowledge is interpreted a collection of elastic or, equivalently, fuzzy constraint on a collection of variables. • Inference is viewed as a process of propagation of elastic con straints. • Any logical system can be fuzzified. There are two main characteristics of fuzzy systems that give them better performance für specific applications.

Multi-Objective Group Decision Making

Methods, Software and Applications with Fuzzy Set Techniques(With CD-ROM)

Author: Jie Lu

Publisher: World Scientific

ISBN:

Category: Computers

Page: 408

View: 660

This book proposes a set of models to describe fuzzy multi-objective decision making (MODM), fuzzy multi-criteria decision making (MCDM), fuzzy group decision making (GDM) and fuzzy multi-objective group decision-making problems, respectively. It also gives a set of related methods (including algorithms) to solve these problems. One distinguishing feature of this book is that it provides two decision support systems software for readers to apply these proposed methods. A set of real-world applications and some new directions in this area are then described to further instruct readers how to use these methods and software in their practice. Contents: Decision Making, Decision Support Systems, and Fuzzy Sets:Decision MakingMulti-Objective and Multi-Attribute Decision MakingGroup Decision MakingDecision Support SystemsFuzzy Sets and SystemsFuzzy Multi-Objective Decision Making:Fuzzy MODM ModelsFuzzy MODM MethodsFuzzy Multi-Objective DSSFuzzy Group Decision Making:Fuzzy MCDMFuzzy Group Decision MakingA Web-Based Fuzzy Group DSSFuzzy Multi-Objective Group Decision Making:Multi-Objective Group DSSFuzzy Multi-Objective Group DSSApplications:Environmental Economic Load DispatchTeam Situation AwarenessReverse Logistics Management Readership: Final year undergraduates, graduate and postgraduate students in business management, computer science, fuzzy logic, artificial intelligence and related areas. Keywords:Multi-Objective Decision Making;Group Decision Making;Multi-Criteria Decision Making;Decision Support Systems;Fuzzy SetKey Features:Describes a complete set of models, methods and algorithms with fuzzy set techniques not only for solving fuzzy MODM, fuzzy MCDM and fuzzy GDM problems, but also for solving general MODM, MCDM and GDM problemsFeatures two decision support systems (DSSs) for a fuzzy multi-objective DSS and a fuzzy group DSS on how to apply, design and implement such kinds of DSSs in practiceHighlights various applications of proposed decision-making methods and DSS software including power markets, team situation awareness, and logistics management, from the practical point of viewReveals new directions of DSSs — online customer DSSs and perceptive DSSs

Expert Systems in Structural Safety Assessment

Proceedings of an International Course October 2-4, 1989, Stuttgart, FRG

Author: Aleksandar S. Jovanovic

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 493

View: 336

Structural safety of industrial systems and components raises a steadily growing public, scientific and engineering interest, and causes permanent development of methods and techniques used for its assessment. In addition to the well established engineering methods, applied in the field, several new methods and tools have emerged recently. Among them, the most novel ones are probably those related to expert system applica tions, appearing as an important possible improvement of the current engineering practice. The issue has been addressed by the international course EXPERT SYSTEMS IN STRUCTURAL SAFETY ASSESSMENT organized by MPA Stuttgart and JRC Ispra (Stuttgart, October 2-4, 1989), and the proceedings of the course are contained in this volume of the Lecture Notes ill Engineering. The contributions (invited lectures) tackle the issues usually confronting developers and users of expert systems applied in structural engineering, i.e. in structural safety and integrity assessment. Both the book and the course are a combination of a tutorial and of presentation of the current achievements in the field. Starting from the basic elements of expert systems (knowledge based systems), the book should "guide" the reader up to the applications in various particular sub-domains.

Fuzzy Set Theory — and Its Applications

Author: Hans-Jürgen Zimmermann

Publisher: Springer Science & Business Media

ISBN:

Category: Business & Economics

Page: 399

View: 693

Since its inception 20 years ago the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of this theory can be found in artificial intelligence, computer science, control engineering, decision theory, expert systems, logic, management science, operations research, pattern recognition, robotics and others. Theoretical advances, too, have been made in many directions, and a gap has arisen between advanced theoretical topics and applications, which often use the theory at a rather elementary level. The primary goal of this book is to close this gap - to provide a textbook for courses in fuzzy set theory and a book that can be used as an introduction. This revised book updates the research agenda, with the chapters of possibility theory, fuzzy logic and approximate reasoning, expert systems and control, decision making and fuzzy set models in operations research being restructured and rewritten. Exercises have been added to almost all chapters and a teacher's manual is available upon request.

Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach

Author: Bilal M. Ayyub

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 371

View: 646

Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.

Fuzzy Sets and Interactive Multiobjective Optimization

Author: Masatoshi Sakawa

Publisher: Springer Science & Business Media

ISBN:

Category: Mathematics

Page: 308

View: 212

The main characteristics of the real-world decision-making problems facing humans today are multidimensional and have multiple objectives including eco nomic, environmental, social, and technical ones. Hence, it seems natural that the consideration of many objectives in the actual decision-making process re quires multiobjective approaches rather than single-objective. One ofthe major systems-analytic multiobjective approaches to decision-making under constraints is multiobjective optimization as a generalization of traditional single-objective optimization. Although multiobjective optimization problems differ from single objective optimization problems only in the plurality of objective functions, it is significant to realize that multiple objectives are often noncom mensurable and conflict with each other in multiobjective optimization problems. With this ob servation, in multiobjective optimization, the notion of Pareto optimality or effi ciency has been introduced instead of the optimality concept for single-objective optimization. However, decisions with Pareto optimality or efficiency are not uniquely determined; the final decision must be selected from among the set of Pareto optimal or efficient solutions. Therefore, the question is, how does one find the preferred point as a compromise or satisficing solution with rational pro cedure? This is the starting point of multiobjective optimization. To be more specific, the aim is to determine how one derives a compromise or satisficing so lution of a decision maker (DM), which well represents the subjective judgments, from a Pareto optimal or an efficient solution set.

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