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2012 | Buch

Introduction to Type-2 Fuzzy Logic in Neural Pattern Recognition Systems Kapitel lesen Erstes Kapitel lesen

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

verfasst von: Patricia Melin

Verlag: Springer Berlin Heidelberg

Buchreihe : Studies in Computational Intelligence

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Basic Concepts and Theory

Frontmatter
Introduction to Type-2 Fuzzy Logic in Neural Pattern Recognition Systems
Abstract
We describe in this book, new methods for building intelligent systems for pattern recognition 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 [37, 57]. In this book, we are extending the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic [12]. 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 in solving pattern recognition problems [59].
Patricia Melin
Type-1 and Type-2 Fuzzy Inference Systems for Images Edge Detection
Abstract
Edges detection in digital images is a problem that has been solved by means of the application of different techniques from digital signal processing, also the combination of some of these techniques with Fuzzy Inference System (FIS) has been experienced [64]. In this chapter a new FIS Type-2 method is implemented for the detection of edges and the results of three different techniques for the same intention are compared [65].
Patricia Melin
Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks
Abstract
The combination of Soft Computing techniques allows the improvement of intelligent systems with different hybrid approaches [12, 57, 59]. In this work we consider two parts of a Modular Neural Network for image recognition, where a Type-2 Fuzzy Inference System (FIS 2) makes a great difference. The first FIS 2 is used for feature extraction in training data, and the second one to find the ideal parameters for the integration method of the modular neural network. Once again fuzzy logic is shown to be a tool that can help improve the results of a neural system, when facilitating the representation of the human perception.
Patricia Melin
Method for Response Integration in Modular Neural Networks Using Type-2 Fuzzy Logic
Abstract
We describe in this chapter a new method for response integration in modular neural networks using type-2 fuzzy logic [59]. The modular neural networks were used in human person recognition [64, 80]. Biometric authentication is used to achieve person recognition. Three biometric characteristics of the person are used: face, fingerprint, and voice [32]. A modular neural network of three modules is used. Each module is a local expert on person recognition based on each of the biometric measures. The response integration method of the modular neural network has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. We show in this chapter the results of a type-2 fuzzy approach for response integration that improves performance over type-1 fuzzy logic approaches.
Patricia Melin

Modular Neural Networks in Pattern Recognition

Frontmatter
Modular Neural Networks for Person Recognition Using the Contour Segmentation of the Human Iris
Abstract
This chapter presents three modular neural network architectures as systems for recognizing persons based on the iris biometric measurement of humans [80]. In these systems, the human iris database is enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The inputs to the modular neural networks are the processed iris images and the output is the number of the person identified. The integration of the modules was done with a gating network method [59].
Patricia Melin
Modular Neural Networks for Human Recognition from Ear Images Compressed Using Wavelets
Abstract
This chapter is focused in the human recognition from ear images as biometric using modular neural networks with preprocessing ear images as network inputs [80]. We proposed modular neural network architecture composed of twelve modules, in order to simplify the problem making it smaller. Comparing with other biometrics, ear recognition has one of the best performances, even when it has not received much attention [9, 74]. To compare with other existing methods, we used the 2D Wavelet analysis with global thresholding method for compression, and Sugeno Measures and Winner-Takes-All as modular neural network integrator. Recognition results achieved was up to 97%.
Patricia Melin
Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration
Abstract
This chapter describes a modular neural network (MNN) with fuzzy integration for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community [59]. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature; however this problem has proven to be a very difficult task. In this work, we propose a MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined using a Sugeno fuzzy integral and a fuzzy inference system [65]. The experimental results obtained using a database of 30 individual’s shows that the modular architecture can achieve a very high 99.33% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system. Furthermore we consider the verification of signatures as false acceptance, false rejection and error recognition of the MNN.
Patricia Melin
Interval Type-2 Fuzzy Logic for Module Relevance Estimation in Sugeno Response Integration of Modular Neural Networks
Introduction
Aggregation has the purpose of making simultaneous use of different pieces of information provided by several sources in order to come to a conclusion or a decision. The aggregation operators are mathematical objects that have the function of reducing a set of numbers into a unique representative number, and any aggregation or fusion process done with a computer underlies numerical aggregation [64].
Patricia Melin

Optimization of Modular Neural Networks for Pattern Recognition

Frontmatter
Optimization of Fuzzy Response Integrators in Modular Neural Networks with Hierarchical Genetic Algorithms
Abstract
In this chapter we describe the development of Fuzzy Response Integrators of Modular Neural Networks (MNN) for Face, Fingerprint and Voice Recognition, and their Optimization with a Hierarchical Genetic Algorithm (HGA). The optimization of the integrators consists of optimizing their membership functions, fuzzy rules, type of model (Mamdani or Sugeno), type of fuzzy logic (type-1 or type-2). The MNN architecture consists of three modules; face, fingerprint and voice [80]. Each of the modules is divided again into three sub modules. The same information is used as input to train the sub modules. Once we have trained and tested the MNN modules, we proceed to integrate these modules with an optimized fuzzy integrator. In this paper we show that using a HGA as an optimization technique for the fuzzy integrators is a good option to solve MNN integration problems [59, 80, 93].
Patricia Melin
Modular Neural Network with Fuzzy Response Integration and Its Optimization Using Genetic Algorithms for Human Recognition Based on Iris, Ear and Voice Biometrics
Abstract
In this chapter we describe the application of a Modular Neural Network (MNN) for iris, ear and voice recognition for a database of 77 persons. The proposed MNN architecture with which we are working consists of three modules; iris, ear and voice [80]. Each module is divided in other three sub modules. Each sub module contains different information, which, the first 26 individuals are considered in module 1, the following 26 individuals in module 2 and the last 25 in module 3. We considered the integration of each biometric measure separately. Later, we proceed to integrate these modules with a fuzzy integrator [59]. Also, we performed optimization of the modular neural networks and the fuzzy integrators using genetic algorithms, and comparisons were made between optimized results and the results without optimization.
Patricia Melin
A Comparative Study of Type-2 Fuzzy System Optimization Based on Parameter Uncertainty of Membership Functions
Abstract
A comparative study of type-2 fuzzy inference systems optimization as an integration method of Modular Neural Networks (MNNs) is presented [32]. The optimization method for type-2 fuzzy systems is based on the footprint of uncertainty (FOU) of the membership functions. We use different benchmark problems to test the optimization method for the fuzzy systems [34, 35]. First, we tested the methodology by manually incrementing the percentage in the FOU, later we apply a Genetic Algorithm to find the optimal type-2 fuzzy system. We show the comparative results obtained for the benchmark problems.
Patricia Melin
Neural Network Optimization for the Recognition of Persons Using the Iris Biometric Measure
Abstract
This chapter describes the application of modular neural network architecture for the recognition of persons using the human iris images [80]; the iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA). We show the results of testing the modular neural network, its optimization using genetic algorithms and the integration with the methods of gating network, type-1 fuzzy integration, and fuzzy integration optimized by genetic algorithms. Simulation results show a good identification using the fuzzy integrators and the best structure found by the genetic algorithm.
Patricia Melin
Optimization of Neural Networks for the Accurate Identification of Persons by Images of the Human Ear as Biometric Measure
Abstract
This chapter describes the application of modular neural network architecture to improve the recognition of persons using Ear images as a biometric measure [80]. The database used was obtained from the University of Science and Technology Beijing (USTB). We show the results obtained with the modular neural network, its optimization using genetic algorithms and their integration using different methods: Winner Takes All (WTA), type-1 fuzzy integration and fuzzy integration optimized by genetic algorithms. The behavior of the simulations shows a good identification, using the appropriate pre-processing, fuzzy integrators and the best structure found by the genetic algorithm.
Patricia Melin
Backmatter
Metadaten
Titel
Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition
verfasst von
Patricia Melin
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-24139-0
Print ISBN
978-3-642-24138-3
DOI
https://doi.org/10.1007/978-3-642-24139-0

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