一种基于田口方法的神经网络架构优化新方法：在弹性短纤维复合材料中的应用
A novel Taguchi-based approach for optimizing neural network architectures: application to elastic short fiber composites
AI总结：
本研究创新性地应用田口实验设计方法优化人工神经网络结构，以预测短纤维增强复合材料的弹性能，在减少计算量的同时提升了预测精度，为计算资源受限场景下的机器学习算法优化提供了有效工具。
摘要：
本研究提出了一种创新性地应用田口实验设计方法来优化人工神经网络（ANN）模型结构的方法，用于预测短纤维增强复合材料的弹性能。主要目标是在增强预测精度的同时，最小化超参数优化所需的计算工作量。利用稳健的实验设计框架，对ANN模型的结构进行了优化。这本质上是识别出一种超参数组合，该组合能以最少的算法运行次数产生最佳的预测精度，从而实现所需计算工作量的显著减少。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，还能大幅提升算法性能。这些结果突显了田口方法作为优化机器学习算法的强大工具的潜力，特别是在计算资源有限的场景下。本研究的意义深远，为未来在优化不同算法以提升精度和计算效率方面的研究提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
