SE-MLP Model for Predicting Prior Acceleration Features in Penetration Signals

TL;DR

This paper introduces SE-MLP, a novel neural network architecture with attention mechanisms and residual connections, enabling fast and accurate prediction of penetration acceleration features from physical parameters, reducing reliance on long simulations.

Contribution

The paper presents SE-MLP, a new model that improves prediction speed and accuracy of penetration features by integrating channel attention and residual structures.

Findings

SE-MLP outperforms traditional MLP, XGBoost, and Transformer models in accuracy.

The model demonstrates strong generalization and stability across different conditions.

Predicted acceleration features closely match measured data within engineering tolerances.

Abstract

Accurate identification of the penetration process relies heavily on prior feature values of penetration acceleration. However, these feature values are typically obtained through long simulation cycles and expensive computations. To overcome this limitation, this paper proposes a multi-layer Perceptron architecture, termed squeeze and excitation multi-layer perceptron (SE-MLP), which integrates a channel attention mechanism with residual connections to enable rapid prediction of acceleration feature values. Using physical parameters under different working conditions as inputs, the model outputs layer-wise acceleration features, thereby establishing a nonlinear mapping between physical parameters and penetration characteristics. Comparative experiments against conventional MLP, XGBoost, and Transformer models demonstrate that SE-MLP achieves superior prediction accuracy, generalization, and stability. Ablation studies further confirm that both the channel attention module and residual structure contribute significantly to performance gains. Numerical simulations and range recovery tests show that the discrepancies between predicted and measured acceleration peaks and pulse widths remain within acceptable engineering tolerances. These results validate the feasibility and engineering applicability of the proposed method and provide a practical basis for rapidly generating prior feature values for penetration fuzes.