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

Jonathan Amezcua

ISBN 10: 3319737724 / ISBN 13: 9783319737720

Published by Springer, Berlin, Springer International Publishing, Springer, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783319737720

Bibliographic Details

• Title: New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic
• Publisher: Springer, Berlin, Springer International Publishing, Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 3319737724
• ISBN 13: 9783319737720
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 236W x 158H x 4D millimeters 236 millimeters width by 158 millimeters height by 4 millimeters depth
• Weight: 156 grams

About this title

Synopsis

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.