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Research on Evaluation Method Used to Quality Performance of Missile Weapon Based on Rough Set Rule Extraction

机译:基于粗糙集规则提取的导弹武器质量性能评估方法研究

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摘要

The quality performance evaluation of long term stored missile weapon is very necessary for the capacity determination of equipment support and combat mission accomplishment ability. Because the classical evaluation methods are affected by index system acquirement, modeling method, experts' resources restriction, etc., the objectivity and correctness of evaluation results are decreased. In order to overcome the shortcomings of classical methods, we proposed a new quality performance evaluation method for missile weapon which based on rough set theory and other related machine learning methods. The method just depends on history quality data of missile weapon in life cycle, firstly, it analyst and extract evaluation rules automatically from this quality data, then the current quality performance of missile weapon can be evaluated by the extracted rules. The theoretical foundation of the evaluation method is presented and the algorithm is implemented. Evaluation results of two calculation examples which have small data sets and large data sets have demonstrated that the proposed method is effective and correct.
机译:长期储存导弹武器的质量性能评估对于确定装备保障能力和作战任务完成能力十分必要。由于经典的评估方法受到指标体系的获取,建模方法,专家资源的限制等因素的影响,降低了评估结果的客观性和正确性。为了克服传统方法的不足,提出了一种基于粗糙集理论和其他相关机器学习方法的导弹武器质量性能评估方法。该方法仅依赖于生命周期内导弹武器的历史质量数据,首先对其进行分析并自动从质量数据中提取评估规则,然后可以通过提取的规则对导弹武器的当前质量性能进行评估。提出了评估方法的理论基础,并实现了算法。通过对两个具有较小数据集和较大数据集的计算示例进行评估,结果表明该方法是有效和正确的。

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