# A novel Taguchi-based approach for optimizing neural network architectures: application to elastic short fiber composites

arXiv: https://arxiv.org/abs/2407.19802 · HTML: https://mlsanity.com/paper/2407.19802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2024.

## Built on by (most cited first, 2 of 2)

- [A comparative study of transformer models and recurrent neural networks for path-dependent composite materials](https://mlsanity.com/paper/2603.00092/) (2026)
- [Empirical Evaluation of No Free Lunch Violations in Permutation-Based Optimization](https://mlsanity.com/paper/2603.03613/) (2026)

## Abstract (arXiv)

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.