Abstract
In this chapter we describe the application of a full model of PSO as an optimization method for modular neural networks with the LVQ algorithm in order to find the optimal parameters of a modular architecture for the classification of arrhythmias. Simulation results show that this modular model optimized with PSO achieves acceptable classification rates for the MIT-BIH arrhythmia database with 15 classes.
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Amezcua, J., Melin, P. (2014). Optimization of Modular Neural Networks with the LVQ Algorithm for Classification of Arrhythmias Using Particle Swarm Optimization. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_21
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DOI: https://doi.org/10.1007/978-3-319-05170-3_21
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