Ebook: Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.

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This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.

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This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Introduction to Type-2 Fuzzy Logic in Neural Pattern Recognition Systems....Pages 3-6
Type-1 and Type-2 Fuzzy Inference Systems for Images Edge Detection....Pages 7-20
Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks....Pages 21-28
Method for Response Integration in Modular Neural Networks Using Type-2 Fuzzy Logic....Pages 29-39
Front Matter....Pages 41-41
Modular Neural Networks for Person Recognition Using the Contour Segmentation of the Human Iris....Pages 43-59
Modular Neural Networks for Human Recognition from Ear Images Compressed Using Wavelets....Pages 61-75
Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration....Pages 77-92
Interval Type-2 Fuzzy Logic for Module Relevance Estimation in Sugeno Response Integration of Modular Neural Networks....Pages 93-105
Front Matter....Pages 107-107
Optimization of Fuzzy Response Integrators in Modular Neural Networks with Hierarchical Genetic Algorithms....Pages 109-126
Modular Neural Network with Fuzzy Response Integration and Its Optimization Using Genetic Algorithms for Human Recognition Based on Iris, Ear and Voice Biometrics....Pages 127-144
A Comparative Study of Type-2 Fuzzy System Optimization Based on Parameter Uncertainty of Membership Functions....Pages 145-161
Neural Network Optimization for the Recognition of Persons Using the Iris Biometric Measure....Pages 163-184
Optimization of Neural Networks for the Accurate Identification of Persons by Images of the Human Ear as Biometric Measure....Pages 185-204
Back Matter....Pages -

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This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Introduction to Type-2 Fuzzy Logic in Neural Pattern Recognition Systems....Pages 3-6
Type-1 and Type-2 Fuzzy Inference Systems for Images Edge Detection....Pages 7-20
Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks....Pages 21-28
Method for Response Integration in Modular Neural Networks Using Type-2 Fuzzy Logic....Pages 29-39
Front Matter....Pages 41-41
Modular Neural Networks for Person Recognition Using the Contour Segmentation of the Human Iris....Pages 43-59
Modular Neural Networks for Human Recognition from Ear Images Compressed Using Wavelets....Pages 61-75
Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration....Pages 77-92
Interval Type-2 Fuzzy Logic for Module Relevance Estimation in Sugeno Response Integration of Modular Neural Networks....Pages 93-105
Front Matter....Pages 107-107
Optimization of Fuzzy Response Integrators in Modular Neural Networks with Hierarchical Genetic Algorithms....Pages 109-126
Modular Neural Network with Fuzzy Response Integration and Its Optimization Using Genetic Algorithms for Human Recognition Based on Iris, Ear and Voice Biometrics....Pages 127-144
A Comparative Study of Type-2 Fuzzy System Optimization Based on Parameter Uncertainty of Membership Functions....Pages 145-161
Neural Network Optimization for the Recognition of Persons Using the Iris Biometric Measure....Pages 163-184
Optimization of Neural Networks for the Accurate Identification of Persons by Images of the Human Ear as Biometric Measure....Pages 185-204
Back Matter....Pages -
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