Enhancing Data Prediction Accuracy With Artificial Neural Network Models

Abstract

This study develops an optimized Artificial Neural Network (ANN) model with 7 hidden layers, TanH activation function, and 30 iterations to enhance predictive modeling for nonlinear data analysis in propulsion-related computational applications. Utilizing a dataset of 2,457 records, normalized via the Min-Max method, the model predicts key performance parameters with high accuracy, achieving R²=0.9864, RMSE=0.0110, and MAD=0.004849922 on the validation set. Compared to benchmark methods, the ANN outperforms Bootstrap Forest (R²=0.9793, RMSE=0.0136, MAD=0.005890918) and Linear Regression (R²=0.6334, RMSE=0.0574, MAD=0.031582035). Significant input variables, such as normalized operational conditions (p<0.0001) and system configuration (p<0.0001), drive the model’s performance, supporting efficient computational analysis. These findings provide a robust tool for optimizing propulsion system design, contributing to advancements in computational methods for aerospace applications.