Amezcua / Melin / Castillo New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic

1. Auflage 2018
ISBN: 978-3-319-73773-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 73 Seiten, eBook

Reihe: SpringerBriefs in Applied Sciences and Technology

In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic.  This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types ofsoil. Both datasets show interesting features that makes them interesting for testing new classification methods.

Zielgruppe

Research

Autoren/Hrsg.

Amezcua, Jonathan

Melin, Patricia

Castillo, Oscar

Inhaltsverzeichnis

Introduction.- Theory and Background.- Problem Statement.- Proposed Classification Method.- Simulation Results.- Conclusions.