This study presents an innovative application of the Taguchi design of experiment method to optimize the structure of an Artificial Neural Network (ANN) model for the prediction of elastic properties of short fiber reinforced composites. The main goal is to minimize the required computational effort for hyperparameter optimization while enhancing the prediction accuracy. Utilizing a robust design of experiment framework, the structure of an ANN model is optimized. This essentially is the identification of a combination of hyperparameters that yields an optimal predictive accuracy with the fewest algorithmic runs, thereby achieving a significant reduction of the required computational effort. Our findings demonstrate that the Taguchi method not only streamlines the hyperparameter tuning process but also could substantially improve the algorithm's performance. These results underscore the potential of the Taguchi method as a powerful tool for optimizing machine learning algorithms, particularly in scenarios where computational resources are limited. The implications of this study are far-reaching, offering insights for future research in the optimization of different algorithms for improved accuracies and computational efficiencies.

翻译：本研究提出了一种创新的田口实验设计方法应用，用于优化预测短纤维增强复合材料弹性性能的人工神经网络（ANN）模型结构。其主要目标是在提高预测精度的同时，最小化超参数优化所需的计算量。利用稳健的实验设计框架，我们对ANN模型的结构进行了优化。这本质上就是识别一组能以最少的算法运行次数获得最佳预测精度的超参数组合，从而显著减少所需的计算量。我们的研究结果表明，田口方法不仅简化了超参数调优过程，还能显著提高算法的性能。这些结果突显了田口方法作为优化机器学习算法强大工具的潜力，特别是在计算资源有限的情况下。本研究的意义深远，为未来优化不同算法以提高精度和计算效率的研究提供了见解。