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

  • Book
  • © 2012

Overview

Authors:
  1. Patricia Melin

    1. Tijuana Institute of Technology, Tijuana, Mexico

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  • Recent research on Modular Neural Networks and Type-2 Fuzzy Systems
  • Presents important applications to Pattern Recognition
  • Written by a leading expert in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 389)

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About this book

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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Keywords

Table of contents (13 chapters)

  1. Front Matter

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  4. Optimization of Modular Neural Networks for Pattern Recognition

    1. Front Matter

      Pages 107-107
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    2. Optimization of Fuzzy Response Integrators in Modular Neural Networks with Hierarchical Genetic Algorithms

      • Patricia Melin
      Pages 109-126

    3. Modular Neural Network with Fuzzy Response Integration and Its Optimization Using Genetic Algorithms for Human Recognition Based on Iris, Ear and Voice Biometrics

      • Patricia Melin
      Pages 127-144

    4. A Comparative Study of Type-2 Fuzzy System Optimization Based on Parameter Uncertainty of Membership Functions

      • Patricia Melin
      Pages 145-161

    5. Neural Network Optimization for the Recognition of Persons Using the Iris Biometric Measure

      • Patricia Melin
      Pages 163-184

    6. Optimization of Neural Networks for the Accurate Identification of Persons by Images of the Human Ear as Biometric Measure

      • Patricia Melin
      Pages 185-204

  5. Back Matter

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Authors and Affiliations

  • Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

Bibliographic Information

  • Book Title: Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

  • Authors: Patricia Melin

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-642-24139-0

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer Berlin Heidelberg 2012

  • Hardcover ISBN: 978-3-642-24138-3Published: 18 October 2011

  • Softcover ISBN: 978-3-642-27027-7Published: 30 November 2013

  • eBook ISBN: 978-3-642-24139-0Published: 25 October 2011

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: X, 214

  • Topics: Computational Intelligence, Artificial Intelligence, Pattern Recognition

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