74 / 2025-05-30 21:54:25

An Adaptive Decision-making Method for Future Command and Control Systems

Software Definition,Command and Control,Autonomous Adaptive,Decision Tree,Association Rule Mining

一、智能增强的体系工程 > 1.体系工程理论新发展

This research introduces a collaborative optimization methodology aimed at improving autonomous adaptive decision-making within Software-Defined Command and Control (C2) Systems. The proposed framework employs a dynamic decision tree architecture, bolstered by a dual feedback mechanism, and integrates sophisticated association rule mining techniques. The hierarchical structure of the decision tree enables real-time strategy adjustments and ongoing performance enhancements, transforming static decision-making challenges into dynamic tasks of decision tree expansion informed by association rule analysis. To overcome the challenges associated with continuous feature processing, a hybrid data discretization method that combines Kernel Density Estimation (KDE) with K-means clustering is proposed. Furthermore, an improved FP-Growth algorithm that incorporates interest metrics enhances the accuracy of association rule mining by reducing the interference of irrelevant rules. Experimental validation conducted in a radar coverage optimization context demonstrates that this approach outperforms traditional decision tree and random forest methodologies in terms of interpretability, while maintaining similar levels of computational efficiency. This study provides both theoretical insights and practical solutions for achieving agile reconfiguration and optimization of autonomous adaptive capabilities in C2 systems operating under complex battlefield conditions. The findings indicate substantial improvements in decision-making performance and system adaptability.