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Fuzzy logic with engineering applications

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This textbook has been in the making for several years. I have been most fortunate in linding an understanding and excellent publisher in McGraw-Hill. We are pleased to be able to provide this text to the legion of professionals and academics who seek a simple, straightforward teaching tool for the expanding and important technology we summarize with the phrasefuzzy logic. I hope that this new text will be valuable in the training of engineers and technologists at the undergraduate, graduate, and/or pmfessional levels to the extent that the technology becomes not just a funny phrase but rather a practical instrument in the solution of today’s complex problems. Fuzzy logic has come a long way since it was first subjected to technical scmtiny in 1965, when Dr. Lotti Zadeh published his seminal work “Fuzzy sets” in the journal Information and Control. Since that time, the subject has been the focus of many independent research investigations by mathematicians, scientists, and engineers from around the world. Unfortunately, perhaps because of the term’s connotations, fuzzy logic did not receive serious notice in this country until the last decade. The attention currently being paid to fuzzy logic is most likely the result of present popular consumer products employing fuzzy logic. Over the last several years, the Japanese alone have filed for well over 1000 patents in fuzzy logic technology, and they have already grossed billions of U.S. dollar» in the sales of fuzzy logic-based products to consumers the world over. The integration of fuzzy logic with neural networks and genetic algorithms is now making automated cognitive systems a reality in many disciplines. In fact, the reasoning power of fuzzy systems, when integrated with the learning capabilities of artificial neural networks and genetic algorithms, is responsible for new commercial products and processes that are reasonably effective cognitive systems (i.e., systems that can learn and reason). Fuzzy technology is so important in Japan that the word fuzzy was proclaimed by the Japanese as the “Keyword” for the 19905. In a 1989 study focused exclusively on fuzzy logic, the international marketing research firm of Frost & Sullivan projected that fuzzy logic, with an annual growth rate of 20 percent, would be one of the world’s 10 hottest technologies going into the twentyflrst century. The Nat'onal Technical Information Service (NTIS), in its 1990 and [10.03, 30/1/2020] Affandi29/GANESHA_XIII: l99l studies on foreign technology of interest to the United States. found that t’uzzx logic will have a significant future impact. ' Although it is relatively new. I believe that the technology involved in inteL ligent and fuzzy systems is of such a fundamental nature that by the turn of the century it will be standard knowledge for all engineers and scientists. It is this bet lief that has sustained me while preparing this text over the last four years. Interest in fuzzy systems is growing most rapidly among undergraduate students. who are seeking a new field for their graduate and/or professional work. And because Ameri. can campuses are responsible for replenishing a significant percentage of the world‘s supply of technical talent. I see the young professional as the fastest~growing group of potential users of this text. Many of the contributions in fuzzy logic and fuzzy set theory are dispersed over a broad range of scientific journals, providing limited, scattered dissemination and utility of knowledge. Most of these journals and edited texts. largely peer-reviewed and archival in nature, are written for other researchers in the held: as such. they are typically too difficult for the uninitiated reader. More simply. the bulk of publications dealing with the theory and application of fuzzy logic presents material that is too complicated and advanced to be quickly assimilated and put into practice. The pedagogy of this textbook is designed for the professional and academic audience interested primarily in applications of fuzzy logic in engineering and tech nology. In the last three years of teaching courses in fuzzy logic and intelligent sys~ terns at the University of New Mexico and in delivering short courses to industry and national laboratones I have found that the majority of students and practicing professionals are interested in the applications of fuzzy logic to their particular fields. Many of these individuals have expressed frustration with the difhculty in understanding the abstract mathematical terms presented in much of the currently available fuzzy logic literature. Hence, the book is written for an audience primarily at the senior undergraduate and first-year graduate levels. With numerous examples throughout the text, this book is designed to assist the learning process of a broad cross section of technical disciplines. The book is primarily focused on applications. but each of the book’s chapters begins with the rudimentary structure of the underlying mathematics required for a fundamental understanding of the methods illustrated. Most of the text can be covered in a one-semester course at the senior undergraduate level. In fact. most science disciplines and virtually all math and engineering disciplines contain the basic ideas of set theory, mathematics, and predicate logic. which form the only knowledge necessary for a complete understanding of the text. Instructors may want to exclude some or all of the material covered in the last three sections of Chapter 4 (neural networks, genetic algorithms, and inductive reasoning), Chapter 9 (fuzz) nonlinear simulation) and the last three chapters of the text. Chapters 13 (fuzzy con! trol), 14 (miscellaneous topics), and 15 (fuzzy measures) and reserve these topics either as introductory material for a graduate-level course or for additional coverage {or graduate students taking the undergraduate course for graduate credit. The book is organized into two broad categories of chapters. The first cate gory, comprising Chapters 1 through 8 introduces basic concepts of fuzzy logic and operations. The second category, Chapters 9 through 15 illustrates the utility of the fundamental properties of fuzzy sets and fuzzy logic in a host of engineer~ ing paradigms. such as classification,pattern pattern recognition, optimization. nonlinear simulation. knowledge based systems. regressiun. decision making. and possibility theory. Chapter I mtmduccx the basic cunccpt nl‘ fuzziness and distinguishes fuzzy uncenmnt) lmm ntltcl forms of unccnznnty. It also introduces the fundamental idea of set membership. thcrch) laying the foundation for all material that follows, and presents membership l‘unctinns us the format used for expressing set membership. Chapter I rcxicws Ban Kusko‘s “sets as points" idea as a graphical analog in understanding the relationship hcmccn classical (crisp) and fuzzy sets. Chapter 2 nevicws classical set theory and develops the basic ideas of fuzzy sets. Operations. laws. and properties of fuzzy sets are introduced by way of compan'sons with the same entities for classical sets. Chapter 3 develops the ideas of fuzzy relations as a means of both mapping fuzziness from one universe to another and developing fuzzy functions. Various forms of the composition operation for relations are presented. Again, the epistemological approach in Chapter 3 uses comparisons with classical relations in developing and illustrating fuzzy relations. This chapter also illustrates methods to determine numen'cal elements of fuzzy relations, and fuzzy relational equations are developed. Chapter4 discusses membership functions in more detail in terms of their propenies and geometric form and the idea of fuzzification. The chapter provides seven methods of developing membership functions, including methods that make use of the technologies of neural networks, genetiealgorithms, and inductive reasoning. Chapter 5 deals with the routines to convert from fuzzy sets and fuzzy relations to classical sets and classical relations, respectively. Such translation, or conversion, is found to be most useful in dealing with the ubiquitous crisp (binary) world around us. In addition, the most common methods of defuzzihcation are developed and illustrated with examples. Chapter 6 summarizes some typical operations in fuzzy arithmetic, fuzzy numbers, and fuzzy vectors. The extension of fuzziness to nonfuzzy mathematical forms using Zadeh’s extension principle and several approximate methods to implement this principle are illustrated. The algebra of fuzzy vectors is introduced here to be used later in'Chapter 12 in the area of pattern recognition. Chapter 7 introduces the precepts of fuzzy logic, again through a review of the relevant features of classical, or tirst-order predicate, logic. Various logical connectives and operations are illustrated. There is a thorough discussion of the various forms of the implication operation and the composition operation provided in this sham. Approximate reasoning, or reasoning under imprecise (fuzzy) infomation, is also introduced in Chapter 7. Chapter 8 introduces natural language and fuzzy expen (rule-based) systems. Important ideas include set descriptions of linguistic data, rule construction, and 'OSICGraphical methods for inferencing are presented. The fu22y rule-based sys~ (cm are seen as generalized fuzzy relational equations. Beginning the second category of chapters in the book highlighting application . . "3’ Chapter 9 continues With the rule-based format to Introduce fuzzy nonlinear.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

620.001 511 3 Ros F

Penerbit

New York : MCGraw-Hill Book., 1995

Deskripsi Fisik

xx, 600 hlm : ilus ; 24 cm

Bahasa

English

ISBN/ISSN

0-07-113637-1

Klasifikasi

620.001 511 3

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

Engineering mathematic
Fuzzy logic

Info Detail Spesifik

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Pernyataan Tanggungjawab

Timothy J. Ross

Versi lain/terkait

Lampiran Berkas

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