A new method for assessing combat data quality, known as the Double-Weighted Fuzzy Analytic Hierarchy Process (FAHP) optimized by Comparative Value Function (CVF), promises to significantly improve accuracy and reliability during military simulations.
This innovative approach addresses longstanding issues within multi-agent combat simulations, where high-quality data is critically necessary for effective decision-making and operational planning. Researchers Jianwei Wang, Chengsheng Pan, and Qing Zhang, who conducted the study, have found their method can reduce mean squared error to 5.35 compared to previous methods, such as the standard FAHP or artificial neural networks, moving closer to accepted benchmarks.
The study emphasizes the inherent challenges of maintaining data quality amid the complexity of military operations, where data can be both voluminous and often unreliable. Traditional evaluation methods suffer from low accuracy, which can lead to flawed decision-making. Addressing this concern, the authors assert the importance of ensuring data quality is not merely academic; it holds substantial practical relevance for military strategy and operational success.
The method proposed establishes a thorough three-tiered framework for evaluation, wherein the initial stage determines threshold values for combat data quality indicators. These indicators fall under different categories of attributes, such as completeness, accuracy, consistency, timeliness, and security.
Utilizing both subjective methods, like FAHP, and objective strategies such as entropy, the newly developed system attributes weights to these indicators based on actual data correlations and expert assessments. Notably, the CVF is employed to optimize the weighting of indicators, providing a comprehensive analysis of combat data quality.
"This method provides a more accurate evaluation of the quality of multi-agent combat data, offering strong data support for combat simulations and drills," the authors noted.
Experimental results demonstrate the method’s efficacy: by analyzing the quality of combat data across several test scenarios, the new evaluation method consistently outperforms traditional models, achieving enhanced reliability. Its formulation is straightforward, marking it as easily applicable for military professionals tasked with data assessment and analysis.
The research concludes by reiterataing the broad potential applications of this evaluation method within military contexts, particularly for conducting effective combat simulations and refining operational strategies. "The proposed method is easy to understand and implement, with evaluation outcomes closer to standard values, holding significant application potential," they state.
Future research directions will likely explore the incorporation of larger datasets and additional operational scenarios, aimed at broader validation of the double-weighted approach to combat data quality evaluation.