Optimization of Modular Neural Networks with the LVQ Algorithm for Classification of Arrhythmias Using Particle Swarm Optimization
作者： Jonathan Amezcua , Patricia Melin
DOI: 10.1007/978-3-319-05170-3_21
关键词: Learning vector quantization 、 Full model 、 Particle swarm optimization 、 Pattern recognition 、 Modular architecture 、 Meta-optimization 、 Artificial intelligence 、 Modular design 、 Algorithm 、 Artificial neural network 、 Computer science
摘要: In this chapter we describe the application of a full model PSO as an optimization method for modular neural networks with LVQ algorithm in order to find optimal parameters architecture classification arrhythmias. Simulation results show that optimized achieves acceptable rates MIT-BIH arrhythmia database 15 classes.
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