关键词: 建模, 参数校正, 过程工业, 数据挖掘, 精度

Abstract: 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.

Key words: modeling, parameter correction, process industry, data mining, accuracy