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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.
Date of Conference: 17-18 Nov. 2012