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In this paper, a new Fuzzy Learning Vector Quantization (FuzzLVQ) method for classification is presented. FuzzLVQ is a hybrid method based on LVQ neural networks and fuzzy systems. FuzzLVQ was implemented using modular architectures based on a granular approach, to further improve its performance in complex classification problems. The contribution of this research work is the development of the new...
In this paper, a new classification method based on LVQ neural networks and Fuzzy Logic is presented. This new fuzzy LVQ method (FuzzLVQ) mainly focuses on distances between the input vector and the cluster centers, randomly generated, thus the fuzzy system in the FuzzLVQ method is used to determine the shortest distance, and with this information, the cluster center can be approached to input vector...
This paper describes the application of competitive neural networks with the LVQ algorithm for classification of electrocardiogram signals (ECG). For this study we used the MIT–BIH arrhythmia database with 15 classes. Three architectures were developed with a modular approach for classification. Compared with other methods that have been developed for classification of arrhythmias with this same database,...
In this paper, the development of a fuzzy system as the integrating unit in a classification model based on modular learning vector quantization (LVQ) neural networks is presented. The method uses a modular approach and is applied for the classification of different types of arrhythmias. The architecture is composed by three modules, each one is working with five different types of arrhythmias; the...
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