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Journal of System Simulation

Abstract

Abstract: Military intelligence technology is currently the most dynamic frontier and the inevitable trend for the development of unmanned equipment in the future. Aiming at the dual requirements of reliability and real-time performance of unmanned platform autonomous decision-making in complex environments and the shortcomings of existing combat simulation technology based on rule reasoning in terms of dynamics and flexibility, a research method of principle analysis and experimental verification is adopted. Based on the shooting experiment dataset of an unmanned platform, the effective position recognition link of attack decision-making is transformed into a binary classification problem with imbalanced categories in the field of machine learning. The effective position intelligent decision-making model with high real-time performance and flexibility is constructed by using correlation analysis, feature engineering, and model fusion technology. Based on the imbalanced classification architecture of ICGANStacking, directional expansion of minority class samples is proposed to achieve data enhancement and model performance improvement. The experimental results show that the recall rate of the proposed method has increased by 4.1%, the accuracy by 0.4%, and the F1 value by 1.5%, and the AUC value reaches 90.9%, which can meet the real-time performance and reliability requirements of the unmanned platform in performing combat tasks.

First Page

1573

Last Page

1585

CLC

TP181

Recommended Citation

Guo Liqiang, Ma Liang, Zhang Hui, et al. Effective Position Intelligent Decision Method Based on Model Fusion and Generative Network[J]. Journal of System Simulation, 2024, 36(7): 1573-1585.

DOI

10.16182/j.issn1004731x.joss.22-1265

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