摘要
Apriori算法可用于从作战仿真实验数据中挖掘作战规律,但通常需要以大量实验数据为支撑。为减少规律发现的成本,提出一种边实验边挖掘的序贯探索方法,及时发现并确认低支持度、高置信度的"潜在规则",再利用规则库避免重复探索,最终提高规律发现的效率。概述了Apriori算法的原理及其在作战仿真实验中的应用;介绍了利用Apriori算法对探索实验进行序贯控制的思路流程及关键技术;最后通过蒙特卡罗仿真验证了该方法的有效性。为开展规律发现型作战仿真实验提供了新思路。
The Apriori algorithm can be applied to mine operational laws from big warfare simulation exper- iment data. To decrease the cost of law discovery, an sequential experiment method is advanced. The main idea is to discover the low support rules timely during experiments, verify them, use rule base to avoid repetitive ex- ploration and finally enhance the mining efficiency. The principles of the Apriori algorithm and its application in warfare simulation experiment are introduced. The algorithm flow to control the exploratory experiments and some key techniques are described. Finally, a Monte Carlo simulation demonstrates the effectiveness of the con- trol method. It is a new idea to conduct law discovery warfare simulation experiments.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2017年第4期917-923,共7页
Systems Engineering and Electronics
基金
全军军事类研究生资助课题(2014JY328)资助课题
