基于元启发式算法的肌电分类优化

Marcos Aviles, J. Rodríguez-Reséndíz, Danjela Ibrahimi
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引用次数: 5

摘要

这项工作提出了一种基于元启发式的方法,在多层感知器中进行超参数选择,以对肌电信号进行分类。该研究的主要目标是通过优化四个重要的超参数来提高模型的性能:神经元数量、学习率、epoch和训练批次。本文提出的方法表明,使用粒子群优化和灰狼优化器的超参数优化显着提高了多层感知器在肌电运动信号分类中的性能。在验证阶段,最终模型的平均分类率达到93%。得到的结果是有希望的,并且表明所提出的方法可能有助于其他信号处理应用中深度学习模型的优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing EMG Classification through Metaheuristic Algorithms
This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications.
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