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Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation

  • Book
  • © 2016

Overview

Authors:
  1. Daniela Sanchez

    1. Division of Graduate Studies, Tijuana Institute of Tech,Div of Gradu, Tijuana, Mexico

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  2. Patricia Melin

    1. Div of Gdu Stud,CalzTecn sn,Fra.TomAqu, Tijuana Institute of Technology, Tijuana, Mexico

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  • Introduces a new model of a modular neural network
  • based on a granular approach
  • Serves as reference
  • book for scientists and engineers interested in applying soft computing
  • Presents recent research
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-viii
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  2. Introduction

    • Daniela Sanchez, Patricia Melin
    Pages 1-3

  3. Background and Theory

    • Daniela Sanchez, Patricia Melin
    Pages 5-11

  4. Proposed Method

    • Daniela Sanchez, Patricia Melin
    Pages 13-36

  5. Application to Human Recognition

    • Daniela Sanchez, Patricia Melin
    Pages 37-40

  6. Experimental Results

    • Daniela Sanchez, Patricia Melin
    Pages 41-80

  7. Conclusions

    • Daniela Sanchez, Patricia Melin
    Pages 81-81

  8. Back Matter

    Pages 83-101
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Keywords

About this book

In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.

Authors and Affiliations

  • Division of Graduate Studies, Tijuana Institute of Tech,Div of Gradu, Tijuana, Mexico

    Daniela Sanchez

  • Div of Gdu Stud,CalzTecn sn,Fra.TomAqu, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

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