A new classification method based on LVQ neural networks and Fuzzy Logic

Abstract

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 if the classification was correct, or moved away in case of misclassification. This new method was tested for arrhythmia classification; the MIT-BIH arrhythmia dataset was used for this research, which consists of 15 classes. Experiments were conducted in a modular FuzzLVQ architecture with 5 modules, having 3 different classes of the dataset in each module.

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An accurate pattern classification for empty fruit bunch based on the age profile of oil palm tree using neural network

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