基于数据挖掘的仿真模型参数修正

针对工业仿真数学模型参数估计实践中的难点,提出了通过数据挖掘来修正模型参数的新方法。从实际生产的大量数据中挖掘样本,通过数学方法计算模型参数,针对包含噪声的工业生产数据主要采用改进了最小二乘方法来修正参数;根据工业生产数据不完全及常见分布特点,采用分段组合修正参数的方法;通过实际生产的动态过程的历史数据挖掘来估计动态特性的相关参数,模型参数修正与数据挖掘过程交互引导,来缩小海量工业数据中的挖掘范围及提高参数修正所需样本数据的充分性,并建立两者之间互相协调的网络模型。实际案例验证了方法在工程项目中的有效性和实用性,表明这种方法能大幅提高仿真精度

Parameter correction of simulation model based on data mining

Concerning the difficulties of parameter estimation for industrial modeling in practice, an innovative approach through data mining to correct parameter of model was proposed. Mining data from a large number of actual data accumulated in production process could be used for correcting parameter through statistical method. The improved method of least square was used for industrial data which contained noise. In view of the characteristics of industrial data, such as incompletion and common distribution, parameter should be segmented and combined to be corrected. For dynamic compensation of statistical model, dynamic parameter can be estimated through data mining of historical dynamic process. Parameter correction and data mining should be interactive with each other. To reduce the scope of massive data mining and improve sufficiency of sample data required for parameter correction, the network model of co-ordination was designed. It is shown in actual cases that this method is efficient and practical. The accuracy of simulation can be greatly improved through this method.