Recent Developments in Fuzzy Logic and Fuzzy Sets

This book provides a timely and comprehensive overview of current theories and methods in fuzzy logic, as well as relevant applications in a variety of fields of science and technology. Dedicated to Lotfi A. Zadeh on his one year death anniversary, the book goes beyond a pure commemorative text. Yet, it offers a fresh perspective on a number of relevant topics, such as computing with words, theory of perceptions, possibility theory, and decision-making in a fuzzy environment. Written by Zadeh's closest colleagues and friends, the different chapters are intended both as a timely reference guide and a source of inspiration for scientists, developers and researchers who have been dealing with fuzzy sets or would like to learn more about their potential for their future research.

Offers a timely snapshot of new fuzzy theories and methods Describes new operations with fuzzy sets Shows applications to clustering, decision making and information processing

Klappentext
This book provides a timely and comprehensive overview of current theories and methods in fuzzy logic, as well as relevant applications in a variety of fields of science and technology. Dedicated to Lotfi A. Zadeh on his one year death anniversary, the book goes beyond a pure commemorative text. Yet, it offers a fresh perspective on a number of relevant topics, such as computing with words, theory of perceptions, possibility theory, and decision-making in a fuzzy environment. Written by Zadeh s closest colleagues and friends, the different chapters are intended both as a timely reference guide and a source of inspiration for scientists, developers and researchers who have been dealing with fuzzy sets or would like to learn more about their potential for their future research.

Inhalt
Chapter 1: Fuzziness in Information Extracted from Tweets' Hashtag and Keywords.- Chapter 2: Why Triangular and Trapezoid Membership Functions: A Simple Explanation.- Chapter 3: Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm.- Chapter 4: Statistical Approach to Fuzzy Cognitive Maps.- Chapter 5: SemiSupervised Learning to Rank with Nonlinear Preference Model.