Uncertain Rule-Based Fuzzy Logic Systems:Introduction and New Directions - Tapa dura

For courses in Neural Networks and Fuzzy Systems; Fuzzy Systems/Control; Fuzzy Logic. The first book of its kind, this text explains how all kinds of uncertainties can be handled within the framework of a common theory and set of design tools-fuzzy logic systems-by moving the original fuzzy logic to the next level-type-2 fuzzy logic. It presents a complete development of both type-1 and type-2 fuzzy logic systems, showing how the expanded and richer fuzzy logic contains the original fuzzy logic within it. The text demonstrates, beyond a reasonable doubt, that when uncertainties are present in a problem, much better performance is obtained by using a type-2 fuzzy logic system than by using a type-1 fuzzy logic system.

• Type-2 fuzzy logic: Breakthrough techniques for modeling uncertainty
• Key applications: digital mobile communications, computer networking, and video traffic classification
• Detailed case studies: Forecasting time series and knowledge mining
• Contains 90+ worked examples, 110+ figures, and brief introductory primers on fuzzy logic and fuzzy sets

Breakthrough fuzzy logic techniques for handling real-world uncertainty.

The world is full of uncertainty that classical fuzzy logic can't model. Now, however, there's an approach to fuzzy logic that can model uncertainty: "type-2" fuzzy logic. In this book, the developer of type-2 fuzzy logic demonstrates how it overcomes the limitations of classical fuzzy logic, enabling a wide range of applications from digital mobile communications to knowledge mining. Dr. Jerry Mendel presents a bottom-up approach that begins by introducing traditional "type-1" fuzzy logic, explains how it can be modified to handle uncertainty, and, finally, adds layers of complexity to handle increasingly sophisticated applications. Coverage includes:

• The sources of uncertainty and the role of membership functions
• Type-2 fuzzy sets: operations, properties, and centroids
• Singleton, non-singleton, and TSK Type 2 fuzzy logic systems
• Comparing "type-2" and "type 1" results
• Extensive applications coverage: digital mobile communications, computer networking, and video traffic classification
• Two start-to-finish case studies: Forecasting time series and knowledge mining

Carefully balanced between theory and design, the book contains over 90 worked examples and more than 110 figures. It is ideal for engineers, scientists, computer science researchers, and mathematicians interested in AI, rule-based systems, and modeling uncertainty. Since it contains brief introductory primers on fuzzy logic and fuzzy sets, it's accessible to virtually anyone with an undergraduate B.S. degree—including computing professionals designing and implementing rule-based systems.

SOFTWARE RESOURCES

Online software includes more than 30 companion MATLAB m-files for implementing a wide variety of type-1 and type-2 fuzzy logic systems.