Application of SVM based on mixtures of kernels in soft-sensor for rare earth countercurrent extraction process

Authors:
Lu Rongxiu;Yang Hui;Zhong Lusheng
Affiliations:
School of Electrical and Electronics Engineering, East China Jiaotong University, Jiangxi, Nanchang;School of Electrical and Electronics Engineering, East China Jiaotong University, Jiangxi, Nanchang;School of Electrical and Electronics Engineering, East China Jiaotong University, Jiangxi, Nanchang
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 2
Cited 0

Robust guaranteed cost control for uncertain linear differential systems of neutral type

Applied Mathematics and Computation
Input space versus feature space in kernel-based methods

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, in virtue of the problem of rare-earth counter-current extraction separation process, in which the real-time online measuring for component content is very difficult, a modeling method of Support Vector Machine (SVM) based on mixtures kernels for rare-earth counter-current extraction separation process is proposed. The model makes use of the character of mixture kernel by more global and local ability and the influence of difference kernels which can be turned by weight factor in the determination of the kernels. According to the results of application, it indicates that the method based on mixtures kernels has both better fitting output and satisfied prediction output, and meets the modeling and control for rare-earth extract process.