Soft-sensor Modeling of Grinding Granularity and Mill Discharge Rate Based on Wavelet Neural Network

被引：0

作者：

Wang Jiesheng ^{[1
]}

Zhu Jing ^{[2
]}

Sun Shifeng ^{[1
]}

机构：

[1] Univ Sci & Technol Liaoning, Sch Elect & Informat Engn, Anshan 114044, Peoples R China

[2] Anshan Normal Univ, Dept Math, Anshan 114005, Peoples R China

来源：

PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年

关键词：

Grinding Process; Soft-sensor; Wavelet Neural Network;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For forecasting the key technology indicators (grinding granularity and mill discharge rate of grinding process, an soft-sensor modeling method based on wavelet neural network is proposed. The assistant variables of the soft-sensor model are selected by analyze the technique characteristic of the grinding process. The structure parameters of the wavelet neural network are optimized by the gradient descent learning algorithm to realize the nonlinear mapping between input and output variables of the discussed soft-sensor model. Simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technical-and-economic indexes and satisfy the real-time control requirements of the grinding process.

引用

页码：7042 / 7046

页数：5