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.

Features

• A self-contained pedagogical approach - Not a handbook.
• An expanded rule-based fuzzy logic - Type-2 fuzzy logic-is able to handle uncertainties because it can model them and minimize their effects; and, if all uncertainties disappear, type-2 fuzzy logic reduces to type-1 fuzzy logic, in much the same way that if randomness disappears, then probability reduces to determinism - shows students how to use fuzzy logic in new ways and how to effectively solve problems that are awash in uncertainties.
• A bottom-up approach - Begins with type-1 fuzzy logic systems and explains how they are modified to the simplest kind of type-2 fuzzy logic system; then add layers of complexity to that system.
• Two short primers on fuzzy sets and fuzzy logic - Amply illustrated with examples). Allows students unfamiliar with fuzzy logic to read and grasp the entire book.
• Two case studies - Carried through the entire book - forecasting of time-series and knowledge-mining using surveys - features applications that are easily understood by all readers.
• Four major applications - Classification of video data working directly with compressed data; equalization of time-varying nonlinear digital communication channels; overcoming co-channel interference and inter-symbol interference for time-varying nonlinear digital communication channels; and connection admission control for asynchronous transfer mode networks - each demonstrates the superiority of type-2 fuzzy logic designs.
• A balance between theory and design - Explains and illustrates design methods for every kind of fuzzy logic system. Using the time-series forecasting case study, the book demonstrates the advantages for using type-2 fuzzy logic systems over type-1 fuzzy logic systems.
• Many worked-out examples (more than 90) and figures (more than 110) clearly illustrates the book's important points.
• Homework problems - At the end of most chapters.
• Readily available supporting software - Developed at USC by two of the author's students and available on-line as freeware. Includes a collection of more than 30 MATLAB m-files that have been organized into three folders: type-1 fuzzy logic systems, general type-2 fuzzy logic systems, and interval type-2 fuzzy logic systems. A “Computation” section at the end of most chapters lets students know exactly which m-files are associated with the contents of the chapter; a short word description is given for the m-file; and Appendix C of the text lists all of the m-files so that students can see the forest from the trees - enables students to immediately be able to use the book's contents.