Open AccessPenetration Depth Prediction Based on Data Fusion
Author(s) -
Jimin Wang,
Mingwei Jiang,
Changzheng Ji,
Lei Zhang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2203/1/012076
Subject(s) - dimensionless quantity , computer science , fusion , experimental data , penetration (warfare) , algorithm , artificial neural network , sensor fusion , fuse (electrical) , data mining , artificial intelligence , mathematics , engineering , statistics , physics , philosophy , linguistics , operations research , mechanics , electrical engineering
This paper presents a penetration depth prediction model based on data fusion. The parameters of the penetration analysis are divided into different evaluation spaces, and then empirical algorithms are evaluated and the better algorithm is selected in each evaluation space. A large number of simulation data is generated to solve the problem of lack of experimental data. Two BP neural network prediction models are built based on experiment data and simulation data, respectively, and the genetic algorithm is used for parameter optimization. Finally, the attention mechanism is used to fuse the two models to generate the final dimensionless penetration depth. The experiment results show that the data fusion model has good prediction accuracy both in the whole parameter space and each evaluation space.