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General Type-2 Fuzzy Edge Detection in the Preprocessing of a Face Recognition System Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Classification of Arrhythmias Using Modular Architecture of LVQ Neural Network and Type 2 Fuzzy Logic

Authors : Jonathan Amezcua, Patricia Melin

Published in: Nature-Inspired Design of Hybrid Intelligent Systems

Publisher: Springer International Publishing

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Abstract

In this paper, a new model for arrhythmia classification using a modular LVQ neural network architecture and a type-2 fuzzy system is presented. This work focuses on the implementation of a type-2 fuzzy system to determine the shortest distance in a LVQ neural network competitive layer. In this work, the MIT-BIH arrhythmia database with 15 classes was used. Results show that using five modules architecture could be a good approach for classification of arrhythmias.

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Metadata
Title
Classification of Arrhythmias Using Modular Architecture of LVQ Neural Network and Type 2 Fuzzy Logic
Authors
Jonathan Amezcua
Patricia Melin
Copyright Year
2017
Book
Nature-Inspired Design of Hybrid Intelligent Systems

Print ISBN: 978-3-319-47053-5

Electronic ISBN: 978-3-319-47054-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47054-2_12

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