2021-03期目录

针对毁伤试验数据少、不均匀、不连续、范围窄等带来的计算精度不高的问题。研究通过数据挖掘技术进行毁伤效应计算。利用数据库管理毁伤数据，通过数据清洗技术识别并清除数据异常点，以保证数据库中数据的质量。建立了算法评价方法以选择最优经验算法。通过特征选择对高维毁伤数据进行降维，确定毁伤效应的主要控制参数进行神经网络学习和k-近邻检索。在此基础上建立基于数据融合的“三阶段”毁伤效应计算模型，可依据试验数据、经验算法和神经网络模型进行毁伤效应计算。实际应用表明，所提出的计算方法，能够满足实际应用需求。

Calculation method of damage effects of underground engineering objectives based on data mining technology

Aiming at low calculation accuracy of damage effect caused by less data, uneven, discontinuity and narrow distribution of damage experimental data, data mining technology is introduced to calculate damage effect. The database manages damage metadata and the data cleaning technology is used to identify and eliminate dead points’ data in order to control the data quality in database. An algorithm evaluation method is established to select the optimal empirical algorithm. The dimensionality reduction of high-dimensional damage data is achieved through feature selection and the main control parameters are chosen to train neural network model and k-nearest neighbor search. The “three-stage” damage effects calculation model based on data fusion has been established. The model can be used to calculate weapon damage effect based on experimental data, the empirical algorithm and the BP neural network model. The software has been developed to complete the damage calculation, and the results shows that the proposed method can meet the needs of practical application.