Type-2 Fuzzy Logic: Theory and ApplicationsWe describe in this book, new methods for building intelligent systems using type-2 fuzzy logic and soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including type-1 fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. In this book, we are extending 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 te- nique offers. We consider in this book the use of type-2 fuzzy logic and traditional SC techniques to solve pattern recognition problems in real-world applications. We c- sider in particular the problems of face, fingerprint and voice recognition. We also consider the problem of recognizing a person by integrating the information given by the face, fingerprint and voice of the person. Other types of applications solved with type-2 fuzzy logic and SC techniques, include intelligent control, intelligent manuf- turing, and adaptive noise cancellation. This book is intended to be a major reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation. |
Contents
Introduction to Type2 Fuzzy Logic | 1 |
Type1 Fuzzy Logic | 5 |
Type2 Fuzzy Logic | 29 |
A Method for Type2 Fuzzy Inference in Control Applications | 44 |
Design of Intelligent Systems with Interval Type2 Fuzzy Logic | 53 |
Method for Response Integration in Modular Neural Networks with Type2 Fuzzy Logic | 77 |
Type2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks for Image Recognition | 87 |
Fuzzy Inference Systems Type1 and Type2 for Digital Images Edge Detection | 95 |
Design of Fuzzy Inference Systems with the Interval Type2 Fuzzy Logic Toolbox | 145 |
Intelligent Control of the Pendubot with Interval Type2 Fuzzy Logic | 155 |
Automated Quality Control in Sound Speakers Manufacturing Using a Hybrid NeurofuzzyFractal Approach | 171 |
A New Approach for Plant Monitoring Using Type2 Fuzzy Logic and Fractal Theory | 186 |
Intelligent Control of Autonomous Robotic Systems Using Interval Type2 Fuzzy Logic and Genetic Algorithms | 203 |
Adaptive Noise Cancellation Using Type2 Fuzzy Logic and Neural Networks | 213 |
Bibliography | 225 |
Appendix | 238 |
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Common terms and phrases
ANFIS chapter considered control system crisp number defined defuzzification describe Edge Detection EDGES equation error experiments fingerprint FLSs fractal dimension fuzzy control fuzzy inference system fuzzy logic approach fuzzy logic controllers Fuzzy Logic Systems fuzzy model fuzzy reasoning fuzzy relations fuzzy rules Gaussian membership functions Gaussian MFs Genetic Algorithms Hybrid Intelligent Systems Image-Quality information signal input variables intelligent systems interval type-2 fuzzy interval type-2 membership ITAE Mamdani MATLAB membership grade Mendel method mobile robot modular neural networks modules non-linear plant Non-linear surface obtained optimized Oscar Castillo output sets parameters Pendubot plant monitoring problem show in Figure Soft Computing sound signal speaker recognition standard deviation Table tion type-1 and type-2 type-1 set type-2 FLC Type-2 Fuzzy Inference type-2 fuzzy logic type-2 fuzzy rules type-2 fuzzy sets type-2 fuzzy system type-2 membership functions type-reduced set uncertain uncertainty values Zadeh