Bøger i Studies in Fuzziness and Soft Computing serien

Starting with the basic concepts of fuzzy sets, and progressing through a normative view on possibility distributions and OWA operators in multiple-criteria decisions, this volume sets out five applications of the theory, based on complex, real-world problems.

It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image.

Information retrieval (IR) aims at defining systems able to provide a fast and effective content-based access to a large amount of stored information. A promising direction to increase the effectiveness of IR is to model the concept of "partially intrinsic" in the IR process and to make the systems adaptive, i.e.

The area known as fuzzy databases covers a bunch of topics among which: -flexible queries addressed to regular databases, -the extension of the notion of a functional dependency, -data mining and fuzzy summarization, -querying databases containing imperfect attribute values represented thanks to possibility distributions.

In the past decade a critical mass of work that uses fuzzy logic for autonomous vehicle navigation has been reported.

Dynamic programming, advo cated in Bellmans book "Dynamic programming" (1957), is one of the most powerful tools to deal with dynamics in systems, and Bellman and Zadeh has proposed the optimality principle in fuzzy decision making by (1970) introducing fuzzy dynamic programming.

At the beginning of the new millennium, fuzzy logic opens a new challenging perspective in information processing. This perspective emerges out of the ideas of the founder of fuzzy logic - Lotfi Zadeh, to develop 'soft' tools for direct computing with human perceptions.

To say that Fuzzy Logic in Medicine, or FLM for short, is an important addi tion to the literature of fuzzy logic and its applications, is an understatement.

Particular attention is paid to situations in which the available information and data may be imprecise, uncertain, incomplete or of linguistic nature. Topics included in the book include preference modelling and decision making, learning, clustering and data mining, information retrieval.

This book is among the very few to be entirely dedicated to the treatment of knowledge imperfection when building intelligent systems. The authors are of the belief that an entire book should be focused on this important aspect of knowledge processing.

A critical companion to the Handbook of Analytical Philosophy of Medicine, this book discusses concepts and propositions introduced in the Handbook, with essays by philosophers and scientists in mathematics, social sciences, computer science and linguistics.

This book offers a meta-theory of knowledge-production process and the logical pathway that connects epistemic possibility to epistemic reality. Covers paradigms for information processing and isolates classical and fuzzy paradigms for comparative analysis.

This book surveys fuzzy rational foundations of the structure of exact and inexact sciences over epistemological space, drawing the conclusion that conditions of the fuzzy paradigm present a methodological unity where every zone of thought has fuzzy covering.

Industrial Engineering (IE) is concerned with the design, improvement, and installation of integrated systems of people, material, equipment, and energy. Industrial engineers face many problems with incomplete and vague information in these systems since the characteristics of these problems often require this kind of information. Fuzzy sets approaches are usually most appropriate when human evaluations and the modeling of human knowledge are needed. IE brings a significant number of applications of fuzzy set theory. After an introductory chapter explaining the recent status of fuzzy sets in IE, this volume involves application chapters on the major seven areas of IE to which fuzzy set theory can contribute. These major application areas are Control and Reliability, Engineering Economics and Investment Analysis, Group and Multi-criteria Decision-making, Human Factors Engineering and Ergonomics, Manufacturing Systems and Technology Management, Optimization Techniques, and Statistical Decision-making. Under these major areas, every chapter includes didactic numerical applications.The authorsAmong many authors in this book, some examples are H.-J. Zimmermann, Janusz Kacprzyk, Hideo Tanaka, Waldemar Karwowski, F. Herrera, C. Kolski, P. Paul Wang, E. Herrera-Viedma, Hung T. Nguyen, Vladik Kreinovich, Jose L. Verdegay, Tomoe Entani, Nikos Tsourveloudis, F. Jimenez.

It examines recent advances in the field, such as aggregation on lattices, penalty-based aggregation and weakly monotone averaging, and extends many of the already existing methods, such as: ordered weighted averaging (OWA), fuzzy integrals and mixture functions.

The book offers a valuable resource for readers involved in theoretical research and practical applications from a range of different fields including game theory, operational research, management science, fuzzy mathematical programming, fuzzy mathematics, industrial engineering, business and social economics.

Starting with a T-S fuzzy system, the objective of this book is to design an H-infinity filter with gain variations that guarantee a prescribed H-infinity performance level. It also shows that the solution can be found by solving linear matrix inequalities.

The presented theories will give data mining practitioners a scientific perspective in data mining and thus provide more insight into their problems, and the provided new data mining topics can be expected to stimulate further research in these important directions.

The aim of this volume is to show how Fuzzy Sets and Systems can help to provide robust and adaptive heuristic optimization algorithms in a variety of situations. The book presents the state of the art and gives a broad overview on the real practical applications that Fuzzy Sets, based on heuristic algorithms, have.

This book presents reports from the forefront of soft computing in the Internet industry and covers important topics in the field such as search engines, fuzzy query, decision analysis and support systems as well as e-business and e-commerce.

After 20 years of pursuing rough set theory and its applications a look on its present state and further prospects is badly needed. Furthermore application of rough sets in data mining, in particular, rule induction methods based on rough set theory is presented and discussed.

One of the most successful methodology that arose from the worldwide diffusion of Fuzzy Logic is Fuzzy Control. At the same time, and very independently from Fuzzy Logic or Fuzzy Control, the birth of the Web has impacted upon almost all aspects of computing discipline.

In a new approach to possibilistic clustering, the sought clustering structure of the set is based directly on the formal definition of fuzzy cluster and possibilistic memberships are determined directly from the values of the pairwise similarity of objects.

The book provides a comprehensive and timely report on the topic of decision making and decision analysis in economics and the social sciences.

This book pays homage to Professor Lotfi A. Zadeh, the "father of fuzzy logic" gathering accounts of personal encounters, research papers inspired by his work, and innovative work contributed by several "third generation fuzzysists or softies."

This mathematically-based introduction to the fascinating topic of Fuzzy Sets and Fuzzy Logic presents a theoretical introduction to Fuzzy Sets based on Mathematical Analysis and Approximation Theory, with well-known applications and advanced topics.

Soft computing includes several intelligent computing paradigms, like fuzzy logic, neural networks, and bio-inspired optimization algorithms. Papers in the third part focus on intelligent techniques for pattern recognition and propose new methods to solve complex pattern recognition problems.

This book contains an edited collection of eighteen contributions on soft and hard computing techniques and their applications to autonomous robotic systems.

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.