Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions
Jerry M. Mendel
- 出版商: Prentice Hall
- 出版日期: 2001-01-01
- 售價: $1,120
- 貴賓價: 9.5 折 $1,064
- 語言: 英文
- 頁數: 576
- 裝訂: Paperback
- ISBN: 0130409693
- ISBN-13: 9780130409690
貴賓價: $1,617Engineering Optimization: Theory and Practice, 4/e (Hardcover)
貴賓價: $4,570Fuzzy Expert Systems and Fuzzy Reasoning (Hardcover)
- 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.
Online software includes more than 30 companion MATLAB m-files for implementing a wide variety of type-1 and type-2 fuzzy logic systems
Table of Contents
3. Membership Functions and Uncertainty.
4. Case Studies.
6. Non-Singleton Type-1 Fuzzy Logic Systems.
III: TYPE-2 FUZZY SETS.
7. Operations on and Properties of Type-2 Fuzzy Sets.
8. Type-2 Relations and Compositions.
9. Centroid of a Type-2 Fuzzy Set: Type-Reduction.
IV: TYPE-2 FUZZY LOGIC SYSTEMS.
10. Singleton Type-2 Fuzzy Logic Systems.
11. Type-1 Non-Singleton Type-2 Fuzzy Logic Systems.
12. Type-2 Non-Singleton Type-2 Fuzzy Logic Systems.
13. TSK Fuzzy Logic Systems.
A. Join, Meet, and Negation Operations For Non-Interval Type-2 Fuzzy Sets.
B. Properties of Type-1 and Type-2 Fuzzy Sets.