In this study, we propose a method to find an optimal combination of hyperparameters to improve the accuracy of respiration pattern recognition in a 1D (Dimensional) convolutional neural network (CNN). The proposed method is designed to integrate with a 1D CNN using the harmony search algorithm. In an experiment, we used the depth of the convolutional layer of the 1D CNN, the number and size of kernels in each layer, and the number of neurons in the dense layer as hyperparameters for optimization. The experimental results demonstrate that the proposed method provided a recognition rate for five respiration patterns of approximately 96.7% on average, which is an approximately 2.8% improvement over an existing method. In addition, the number of iterations required to derive the optimal combination of hyperparameters was 2,000,000 in the previous study. In contrast, the proposed method required only 3652 iterations.

中文翻译：

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基于和谐搜索算法的超参数优化方法，以提高一维CNN人体呼吸模式识别系统的性能。

在这项研究中，我们提出了一种寻找超参数的最佳组合的方法，以提高一维（维）卷积神经网络（CNN）中呼吸模式识别的准确性。该方法被设计为使用和声搜索算法与一维CNN集成。在实验中，我们将一维CNN卷积层的深度，每层中的内核数量和大小以及密集层中神经元的数量用作优化的超参数。实验结果表明，所提出的方法对五个呼吸模式的识别率平均约为96.7％，比现有方法提高了约2.8％。此外，推导超参数最佳组合所需的迭代次数为2,000，在先前的研究中为000。相反，提出的方法仅需要3652次迭代。