On Fuzziness

From the minds of Tom Angleberger, the New York Times bestselling author of the wildly popular Origami Yoda series, and Paul Dellinger, an adult science-fiction writer, comes a funny middle school story with a memorable robot title character. Reluctant readers and robot lovers in elementary and middle school will enjoy this fast-paced read that shows just how strange a place middle school can be, particularly when the new student is a state-of-the-art robot. When Max—Maxine Zelaster—befriends her new robot classmate Fuzzy, part of Vanguard One Middle School’s new Robot Integration Program, she helps him learn everything he needs to know about surviving middle school—the good, the bad, and the really, really, ugly. Little do they know that surviving seventh grade is going to become a true matter of life and death, because Vanguard has an evil presence at its heart: a digital student evaluation system named BARBARA that might be taking its mission to shape the perfect student to extremes! With a strong female main character who will appeal to all readers, Tom Angleberger and Paul Dellinger’s new novel offers readers a fresh take on robots. Fuzzy will find its place in the emerging category of bestselling books featuring robots, including Jon Scieszka’ s Frank Einstein series and James Patterson’s House of Robots. Be sure to check out all of Tom Angleberger’s other acclaimed books for middle-grade readers, including Poop Fountain!; The Rat with the Human Face; Horton Halfpott; Fake Mustache; and the bestselling Origami Yoda series: The Strange Case of Origami Yoda, Darth Paper Strikes Back, The Secret of the Fortune Wookiee, Emperor Pickletine Rides the Bus,Princess Labelmaker to the Rescue, and Jabba the Puppet. For younger readers Tom wrote the picture book McToad Mows Tiny Island, illustrated by John Hendrix, and for chapter book readers, Tom wrote the Inspector Flytrap series, illustrated by his wife Cece Bell.

Mathematics of Fuzziness – Basic Issues introduces a basic notion of ‘fuzziness’ and provides a conceptual mathematical framework to characterize such fuzzy phenomena in Studies in Fuzziness and Soft Computing. The book systematically presents a self-contained introduction to the essentials of mathematics of fuzziness ranging from fuzzy sets, fuzzy relations, fuzzy numbers, fuzzy algebra, fuzzy measures, fuzzy integrals, and fuzzy topology to fuzzy control in a strictly mathematical manner. It contains most of the authors’ research results in the field of fuzzy set theory and has evolved from the authors’ lecture notes to both undergraduate and graduate students over the last three decades. A lot of exercises in each chapter of the book are particularly suitable as a textbook for any undergraduate and graduate student in mathematics, computer science and engineering. The reading of the book will surely lay a solid foundation for further research on fuzzy set theory and its applications.

Readings in Fuzzy Sets for Intelligent Systems is a collection of readings that explore the main facets of fuzzy sets and possibility theory and their use in intelligent systems. Basic notions in fuzzy set theory are discussed, along with fuzzy control and approximate reasoning. Uncertainty and informativeness, information processing, and membership, cognition, neural networks, and learning are also considered. Comprised of eight chapters, this book begins with a historical background on fuzzy sets and possibility theory, citing some forerunners who discussed ideas or formal definitions very close to the basic notions introduced by Lotfi Zadeh (1978). The reader is then introduced to fundamental concepts in fuzzy set theory, including symmetric summation and the setting of fuzzy logic; uncertainty and informativeness; and fuzzy control. Subsequent chapters deal with approximate reasoning; information processing; decision and management sciences; and membership, cognition, neural networks, and learning. Numerical methods for fuzzy clustering are described, and adaptive inference in fuzzy knowledge networks is analyzed. This monograph will be of interest to both students and practitioners in the fields of computer science, information science, applied mathematics, and artificial intelligence.

The developments of fuzzy systems and fuzzy logic is permeating through the diverse branches of science where uncertainty has to be considered laying on the foundations and applicative developments. CIFT and MEPP conferences have been held in different venues in Scandinavia and Italy since 1990, and have stimulated the attention from academia and industry toward the novelties introduced by fuzzy logic and fuzzy systems theory. The papers presented in this volume are concerned with a wide vision of modern perspectives of science. These cover research areas such as management, financial and economic applications, urbanism and ecology, astronomical engineering, medical diagnosis and imaging, and human behavior. Contents:Retrieving Documents from Multiple Information Sources (R Yager & A Rybalov)Basic Principles of Rough Set Analysis (B Matarazzo)Conditional Measures: Old and New (G Coletti & R Scozzafava)Application of a New Fuzzy Identification Algorithm for the Control of a DC to DC Converter (A Luciano et al)Fuzzy Logic and the Engineering of Quality in Electronic Products (B Bosacchi)On Some Order Structures in Fuzzy Modelling (M Fedrizzi et al)Fuzzy Control for Medicine: State of the Art and New Perspectives (S Giove)The Generalised Perceptron is a Fuzzy Neuron and a Fuzzy Rule (L Kallin & P Eklund)Application of MEP-Based Fuzzy Clustering to the Segmentation of Multivariate Medical Images (F Masulli et al)and other papers Readership: Students, engineers, and researchers in fuzzy systems, artificial intelligence, systems/knowledge engineering, biomedical engineering, civil engineering, applied mathematics, materials science, economics/finance and management. keywords:Fuzzy Logic;Fuzzy Modeling;Fuzzy Rule;Fuzzy Clustering

In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge. Fuzzy Image Processing and Applications with MATLAB® presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few. Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation. Minimize Processing Errors Using Dynamic Fuzzy Set Theory This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecision—whether it is in the grey level of the image, geometry of an object, definition of an object’s edges or boundaries, or in knowledge representation, object recognition, or image interpretation. The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others.

The volume "Fuzziness in Database Management Systems" is a highly informative, well-organized and up-to-date collection of contributions authored by many of the leading experts in its field. Among the contributors are the editors, Professors Patrick Bose and Janusz Kacprzyk, both of whom are known internationally. The book is like a movie with an all-star cast. The issue of fuzziness in database management systems has a long history. It begins in 1968 and 1971, when I spent my sabbatical leaves at the IBM Research Laboratory in San Jose, California, as a visiting scholar. During these periods I was associated with Dr. E.F. Codd, the father of relational models of database systems, and came in contact with the developers ofiBMs System Rand SQL. These associations and contacts at a time when the methodology of relational models of data was in its formative stages, made me aware of the basic importance of such models and the desirability of extending them to fuzzy database systems and fuzzy query languages. This perception was reflected in my 1973 ffiM report which led to the paper on the concept of a linguistic variable and later to the paper on the meaning representation language PRUF (Possibilistic Relational Universal Fuzzy). More directly related to database issues during that period were the theses of my students V. Tahani, J. Yang, A. Bolour, M. Shen and R. Sheng, and many subsequent reports by both graduate and undergraduate students at Berkeley.

Readers will get a complete understanding of the notions, techniques, and methods related to the research and developments of fuzzy object-oriented databases from the book, which will serve as a starting point and a reference for their research and developments."--BOOK JACKET.

This book provides a thorough overview of recent methods using higher level information (object or scene level) for advanced tasks such as image understanding along with their applications to medical images. Advanced methods for fuzzy image processing and understanding are presented, including fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. For each methodological aspect covered, illustrations from the medical imaging domain are provided. This is an ideal book for graduate students and researchers in the field of medical image processing.