Type-2 fuzzy logic : theory and applications
Author(s)
Bibliographic Information
Type-2 fuzzy logic : theory and applications
（Studies in fuzziness and soft computing, v. 223）
Springer, c2008
Available at 4 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
This book describes new methods for building intelligent systems using type-2 fuzzy logic and soft computing (SC) techniques. The authors extend the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. This book is intended to be a major reference tool and can be used as a textbook.
Table of Contents
1 Introduction to Type-2 Fuzzy Logic.- 2 Type-1 Fuzzy Logic.- 3 Type-2 Fuzzy Logic.- 4 A Method for Type-2 Fuzzy Inference in Control Applications.- 5 Design of Intelligent Systems with Interval Type-2 Fuzzy Logic.- 6 Method for Response Integration in Modular Neural Networks with Type-2 Fuzzy Logic.- 7 Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks for Image Recognition.- 8 Fuzzy Inference Systems Type-1 and Type-2 for Digital Images Edge Detection.- 9 Systematic Design of a Stable Type-2 Fuzzy Logic Controller.- 10 Experimental Study of Intelligent Controllers Under Uncertainty Using Type-1 and Type-2 Fuzzy Logic.- 11 Evolutionary Optimization of Interval Type-2 Membership Functions Using the Human Evolutionary Model.- 12 Design of Fuzzy Inference Systems with the Interval Type-2 Fuzzy Logic Toolbox.- 13 Intelligent Control of the Pendubot with Interval Type-2 Fuzzy Logic.- 14 Automated Quality Control in Sound Speakers Manufacturing Using a Hybrid Neuro-fuzzy-Fractal Approach.- 15 A New Approach for Plant Monitoring Using Type-2 Fuzzy Logic and Fractal Theory.- 16 Intelligent Control of Autonomous Robotic Systems Using Interval Type-2 Fuzzy Logic and Genetic Algorithms.- 17 Adaptive Noise Cancellation Using Type-2 Fuzzy Logic and Neural Networks.
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