FUZZY SETS UNCERTAINTY AND INFORMATION GEORGE KLIR PDF
Fuzzy SETS,. UNCERTAINTY,. AND. INFORMATION. GEORGE J. KLIR AND TINA A. FOLGER. State University of New York, Binghamton. Prentice Hall. No previous knowledge of fuzzy set theory or information theory is required for an Fuzzy sets, uncertainty, and information / George J. Klir and Tina A. Folger ; Contributor: Folger, Tina A. Digital Description: application/pdf, xi, p. Fuzzy sets, uncertainty and information, by George J. Klir and Tina A. Folger, Prentice Hall, Englewood Cliffs, NJ, Constantin Virgil Negoita. Department of.
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George J. Klir, Tina A. Folger. Fuzzy Sets, Uncertainty and Information George J. Klir, Tina A. Folger. Download Fuzzy Sets, Uncertainty and Information pdf. Fuzzy Sets, Uncertainty and Information George J. Klir, Tina A. Folger books online, books to read online, online library, greatbooks to read, PDF best books to. 30, Issue 3. Citation; References · PDF. SIAM Rev., 30(3), – (3 pages). Fuzzy Sets, Uncertainty, and Information (George J. Klir and Tina A. Folger).
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From until his retirement in he was with SUNY Binghamton where he obtained the rank of distinguished professor in , served as chairman of the Department of Systems Science — and director of the Center for Intelligent Systems — During the academic years — and —, he was a fellow at the Netherlands Institute for Advanced Studies, and in a fellow of the Japan Society for the Promotion of Science. George was a brilliant scholar who had an unusually broad spectrum of interests.
He left a lasting mark in every area in which he worked. During the earlier stages of his career, he conducted research in the areas of systems modeling and simulation, logic design, and computer architecture. Later on his research included, in particular, generalized information theory, fuzzy logic and fuzzy sets, and generalized measures, but also a variety of other topics such as the psychology of concepts or certain aspects in philosophy of science.
At SUNY Binghamton, he supervised 34 completed doctoral dissertations and taught graduate courses on fuzzy systems, generalized information theory, systems problem solving, discrete mathematics, logic design and computer architecture, fault-tolerant computing, automata theory, introduction to systems science, and combinatorial analysis.
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Fuzzy sets, uncertainty, and information
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Folger You can find this publication conveniently here. This is what underlines the basic paradigm shift which is discussed so insightfully in the first chapter of Fuzzy Sets and xi Foreword xii behind this paradigm shift is the realization that traditional two-valued logical systems, crisp set theory and crisp probability theory are inadequate for dealing with imprecision, uncertainty and complexity of the real world. It is this realization that motivates the evolution of fuzzy set theory and fuzzy logic and shapes their role in restructuring the foundations of scientific theories and their applications.
And it is in this perspective that the contents of Fuzzy Seis caul Fuzzy Logic should be viewed, The first part of Fuzzy Sets and Fuzzy Logic provides a very carefully crafted introduction to the basic concepts and techniques of fuzzy set theory.
The exposition is authoritative, rigorous and up-to-date. An important issue which receives a great deal of attention is that of the relationship between fuzzy set theory and alternative methods of dealing with uncertainty.
This is a complex, controversial issue that is close to the heart of Professor lair and which be treats with authority and insight. There is a minor point relating to possibility theory that deserves a brief comment. This is motivated by the observation that in the case of nested focal sets in the Dempster-Shafer theory, possibility measure coincides with plausibility measure, I view this as merely a point of tangency between the Dempster-Shafer theory and possibility theory, since the two theories have altogether different agendas.
Although the authors note that possibility theory can also be introduced via fuzzy set theory, their choice of the theory of evidence as the point of departure makes possibility theory less intuitive and harder to understand, The second part of Fuzzy Sets and Juzzy Logic is in the mainapplications oriented, but it also contains compact and yet insightful expositions of the calculi of fuzzy rules and fuzzy relations.
The applications cover a wide spectrum of topica ranging from fuzzy control and expert systems to information retrieval, pattern recognition and decision analysis. The discussion of applications is thorough and up-tn-date. The book closes with a valuable bibliography of over 1, papers and books dealing with various issues relating to fuzzy sets and fuzzy logic.
To say that Fuzzy Sets and Fuzzy Logic is a major contribution to the literature is an understatement. In most of the current applications of fuzzy logic in the realms of industrial systems and consumer products, what is used is a small subset of fuzzy logic centering on the methodology of fuzzy rules and their induction from observations.
By focusing on this and only this methodology, it is possible to acquire with a low expenditure of time and effort —a working knowledge of fuzzy logic techniques. This is not the route chosen by Professor Klir and Bo Yuan. Their goals are loftier, they have produced a volume that presents an exceptionally thorough, well-organized, authoritative and reador-frienctly oxposition of the methodology of fuzzy sets and fuzzy logic.
The driving force — LoW A. Klir and Tina A. Folger Prentice Hall, It reflects the tremendous advances that have taken place in the areas of fuzzy set theory and fuzzy logic during the period Captured in the book are not only theoretical advances in these areas, but a broad variety of applications of fuzzy sets and fuzzy logic as well.
The primary purpose of the book is to facilitate eduoation in the increasingly important areas of fuzzy set theory and fuzzy logic. It is written as a text for a course at the graduate or upper-division undergraduate level.
Although there is enough material in the text for a two-semester cours; relevant material may be selected, according to the needs of each individual program, for a one-semester course.
The text is also suitable for selfstudy and for short, intensive courses of continuing education: No previous knowledge of fuzzy set theory or fuzzy logic is required for an understanding of the material in this text. Although we assume that the reader is familiar with the basic notions of classical nonftazy set theory, classical two-valued logic, and probability theory, fundamentals of these subject areas are briefly overviewed in the hook.
Basic ideas of neural networks, genetic algorithms, and rough sets, which are occasionally needed in the text, are provided in Appendices A—C. This makes the book virtually selfcontained.
Elementary concepts, including basic types of fuzzy sets, are introduced in Chapter 1, which also contains a discussion of the meaning and significance of the emergence of fuzzy set theory Connections between fuzzy sets and crisp sets are examined in Chapter 2.
It shows how fuzzy sets can be represented by families of crisp sets and how classical mathematical functions can be Razifled. Chapter 3 deals with the various aggregation operations on fuzzy sets. It covers general fuzzy complements, fuzzy intersections I-norms , fuzzy unions t-conorms , and averaging operations.
Fuzzy numbers and arithmetic operations on fuzzy numbers are covered in Chapter 4, where also the concepts of linguistic variables and fuzzy equations are introduced and examined. Basic concepts of fuzzy relations are introduced in Chapter 5 and employed in Chapter 6 for the study of fuzzy relation equations, an important tool for many applications of fuzzy set theory.
Preface xiv Figure El Prerequisite dependencies among chapters of this book. The position of possibility theory within the broader framework of fuzzy measure theory is also examined. Chapter 8 overviews basic aspects of fuzzy logic, including its connection to classical naultivalued logics, the various types of fuzzy propositions, and basic types of fuzzy inference rules.
Chapter 9, the last chapter in Part I, is devoted to the examination of the connection between uncertainty and information, as represented by fuzzy sets, possibility theory, or evidence theory.
The chapter shows how relevant uncertainty and uncertainty-based information can be measured and how these uncertainty measures can be utilized. Part IL which is devoted to applications of fuzzy set theory and fuzzy logic, cons ists of the remaining eight chapters. Chapter 10 cgainincs various methods for constructing membership functions of fuzzy sets, including the increasingly popular use of neural networks.
Chapter 11 is devoted to the use of fuzzy logic for approximate reasoning in expert systems.
It includes a thorough examination of the concept of a fuzzy implication. Fuzzy systems are covered in Chapter 12, including fuzzy controllers, fuzzy automata, and fuzzy neural networks.
Fuzzy techniques in the related areas of clustering. Fuzzy databases, a well developed application area of fuzzy set theory, and the related area of fuzzy retrieval systems are covered in Chapter Basic ideas of the various types of fuzzy decision making are summarized in Chapter Engineering applications other than fuzzy control are touched upon in Chapter 16, and applications in various other areas medicine, economics, etc.
The prerequisite dependencies among the individual chapters and some appendices are expressed by the diagram in Fig. Following the diagram, the reader has ample flexibility ill studying the material. For example, Chapters 3, 5 and 6 may be studied prior to Chapters 2 and 4; Chapter 10 and Appendix A may be studies prior to Chapter 2 and Chapters 4 through 9; etc.
In order to avoid interruptions in the main text, virtually all bibliographical, his bark:al, and other remarks are incorporated in the notes that follow each individual chapter. These notes are uniquely numbered and are only occasionally referred to in the text.
At the graduate level, on the other hand, we encourage coverage of most of these proofs in order to effect a deeper understanding of the material. In all cases, the relevance of the material to the specific area of student interest can be emphasized with additional applicationoriented readings guided by relevant notes in Part H of the text.
Each chapter is followed by a set of exercises, which are intended to enhance an understanding of the material presented in the chapter.
The solutions to a selected subset of these exercises are provided in the instructor's manual, which also contains further suggestions for use of the text under various circumstances. The book contains an extensive bibliography, which covers virtually all relevant books and significant papers published prior to Forgot your username?
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. Google Scholar  Z. Zadeh, Probability measures of fuzzy events, Journal of Mathematical Analysis and Applications 23 These notes are uniquely numbered and are only occasionally referred to in the text. Klir, A principle of uncertainty and information invariance, International Journal of General Systems 17 Yager et al.
Using the limit of information, processing obtained for one gram of mass and one second of processing time, Bremermann then calculates the total number of bits processed by a hypothetical computer the size of the Earth within a time period equal to the estimated age of the Earth. The text is also suitable for selfstudy and for short, intensive courses of continuing education: No previous knowledge of fuzzy set theory or fuzzy logic is required for an understanding of the material in this text.
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