# Sound source ranging using a feed-forward neural network with fitting-based early stopping

arXiv: https://arxiv.org/abs/1904.00583 · HTML: https://mlsanity.com/paper/1904.00583/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Adaptive Control Attention Network for Underwater Acoustic Localization and Domain Adaptation](https://mlsanity.com/paper/2506.17409/) (2025)

## Abstract (arXiv)

When a feed-forward neural network (FNN) is trained for source ranging in an ocean waveguide, it is difficult evaluating the range accuracy of the FNN on unlabeled test data. A fitting-based early stopping (FEAST) method is introduced to evaluate the range error of the FNN on test data where the distance of source is unknown. Based on FEAST, when the evaluated range error of the FNN reaches the minimum on test data, stopping training, which will help to improve the ranging accuracy of the FNN on the test data. The FEAST is demonstrated on simulated and experimental data.