An Adaptive Soft Sensor for Mill Load Measurement based on PCA and FasArt Neural Fuzzy Networks

摘要：

Precise load measurement is important for the supervision of the pulverizing process in thermal power plant. This paper presents an adaptive soft sensor based on PCA and FasArt neural networks to achieve this purpose. PCA is firstly used to compress the input secondary variables and the dimension is reduced from 9 to 3 with little loss of information. Then FasArt model derive the knowledge from the training data and construct the relationships between the input secondary variables and target variable automatically. Experimental results show that the proposed model achieve a high accuracy. Moreover, the model has potential advantage of incremental learning capability.

作者: Gangquan Si Hui Cao Yanbin Zhang Lixin Jia

作者单位: School of Electrical Engineering,Xian JiaoTong University Xian,Shaanxi,710049,P.R.China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机械自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 123-126

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）