Today, Fuzzy Set Theory is the core discipline of so-called ‘soft’ computing, and provides new impetus for research in the field of artificial intelligence. In this fascinating book, the history of Fuzzy Set Theory and the ways it was first used are incorporated into the history of 20th century science and technology. Influences from philosophy, system theory and cybernetics stemming from the earliest part of the 20th century are considered alongside those of communication and control theory from mid-century.

This book is an essential contribution to the description of fuzziness in information systems. Usually users want to retrieve data or summarized information from a database and are interested in classifying it or building rule-based systems on it. But they are often not aware of the nature of this data and/or are unable to determine clear search criteria. The book examines theoretical and practical approaches to fuzziness in information systems based on statistical data related to territorial units. Chapter 1 discusses the theory of fuzzy sets and fuzzy logic to enable readers to understand the information presented in the book. Chapter 2 is devoted to flexible queries and includes issues like constructing fuzzy sets for query conditions, and aggregation operators for commutative and non-commutative conditions, while Chapter 3 focuses on linguistic summaries. Chapter 4 presents fuzzy logic control architecture adjusted specifically for the aims of business and governmental agencies, and shows fuzzy rules and procedures for solving inference tasks. Chapter 5 covers the fuzzification of classical relational databases with an emphasis on storing fuzzy data in classical relational databases in such a way that existing data and normal forms are not affected. This book also examines practical aspects of user-friendly interfaces for storing, updating, querying and summarizing. Lastly, Chapter 6 briefly discusses possible integration of fuzzy queries, summarization and inference related to crisp and fuzzy databases. The main target audience of the book is researchers and students working in the fields of data analysis, database design and business intelligence. As it does not go too deeply into the foundation and mathematical theory of fuzzy logic and relational algebra, it is also of interest to advanced professionals developing tailored applications based on fuzzy sets.

Provides an introduction to the fundamental concepts of fuzziness together with a compilation of recent advances in the application to medicine. The tutorials in the first part of the book range from basic concepts through theoretical frameworks to rule simplification through data clustering methodologies and the design of multivariate rule bases through self-learning by mapping fuzzy systems onto neural network structures. The case studies which follow are representative of the wide range of applications currently pursued in relation to medicine. The majority of applications presented in this book are about bridging the gap between low-level sensor measurements and intermediate or high-level data representations. The book offers a comprehensive perspective from leading authorities world-wide and provides a tantalising glimpse into the role of sophisticated knowledge engineering methods in shaping the landscape of medical technology in the future.

Hier lernen Sie, Expertensysteme auf der Basis von Fuzzy Logic zu konstruieren, die sich für den praktischen Einsatz eignen. Expertensysteme werden zunächst allgemein definiert, und die zugrundeliegende Mathematik wird eingeführt. Regelbasierte Systeme werden gründlicher besprochen als in jedem anderen Buch mit ähnlichem Thema. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen. Von einem zugehörigen ftp-Server können Sie Ergänzungsmaterial abrufen. Für Praktiker und Forscher aus dem akademischen Umfeld gleichermaßen geeignet!

This book is an excellent starting point for any curriculum in fuzzy systems fields such as computer science, mathematics, business/economics and engineering. It covers the basics leading to: fuzzy clustering, fuzzy pattern recognition, fuzzy database, fuzzy image processing, soft computing, fuzzy applications in operations research, fuzzy decision making, fuzzy rule based systems, fuzzy systems modeling, fuzzy mathematics. It is not a book designed for researchers - it is where you really learn the "basics" needed for any of the above-mentioned applications. It includes many figures and problem sets at the end of sections.

The concept of fuzzy logic was conceived by Lotfi Zadeh, a professor at the University of California in Berkeley. His first paper on fuzzy sets in 1965 has motivated researchers and scientists to pursue their studies in this field. Fuzzy logic has emerged as an alternative method to conventional theory in dealing with systems where uncertainty exists. Such systems include engineering applications, economics, business, bio-medical applications etc. A Learner s Guide to Fuzzy Logic Systems introduces the reader to uncertainty-related issues and illustrates the concept of fuzzy sets and operations in a systematic manner. Fuzzy reasoning, fuzzy logic design and numerous applications of fuzzy set theory, including neuro-fuzzy systems and fuzzy genetic algorithms are also covered.This book is primarily intended for undergraduate/postgraduate students and researchers to facilitate education in the ever-increasing field of fuzzy logic. It will also be suitable as a textbook for regular course work as well as for self-study. crowd.

Examines the methodology and algorithms of fuzzy sets considered mainly in the context of control engineering and system modelling and analysis. Special emphasis is focused on the processing of fuzzy information realized with the aid of fuzzy relational structures and their extensions.