Effective Position Intelligent Decision Method Based on Model Fusion and Generative Network
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 binaryclassification 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 ofICGAN-Stacking,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 Fl 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.
military intelligenceunmanned platformmodel fusiongenerative adversarial networkimbalance classification
NETL
NSTL
万方数据
