Autor: Jonathan Amezcua
ISBN-13: 9783319737720
Einband: Book
Seiten: 73
Format: 236x156x7 mm
Sprache: Englisch

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

SpringerBriefs in Applied Sciences and Technology
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Develops a new model for data classification
Introduction.- Theory and Background.- Problem Statement.- Proposed Classification Method.- Simulation Results.- Conclusions.
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 of soil. Both datasets show interesting features that makes them interesting for testing new classification methods.

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Autor: Jonathan Amezcua
ISBN-13:: 9783319737720
ISBN: 3319737724
Erscheinungsjahr: 08.03.2018
Verlag: Springer-Verlag GmbH
Gewicht: 154g
Seiten: 73
Sprache: Englisch
Auflage 2018
Sonstiges: Taschenbuch, 236x156x7 mm, 10 schwarz-weiße und 12 farbige Abbildungen, Bibliographie