Adaptive Least Contribution Elimination Kernel Learning Approach for Rubber Mixing Soft-sensing Modeling
Rubber mixing process is a typical nonlinear fed-batch process without well developed mechanism. Soft-sensing modeling of the mixtures Mooney viscosity is crucial and challenging since this index is an important process criterion to judge the quality of rubber compounds while the measurement of Mooney viscosity is time-consuming and laborious to assay. Furthermore, the mixing process is drifting and volatile even noisy;only few data samples could be used to modeling. In present paper, an adaptive least contribution elimination kernel learning (ALCEKL) approach is proposed to predict the Mooney viscosity. It adopts a sparsity strategy of least contribution elimination and presents a buffer based learning algorithm associated with improved space angle index (SAI) strategy. Experiments on field data indicate that proposed approach is more robust and accurate than other kernelized modeling methods with feasible computational complexity under field circumstances.
component kernel learning rubber mixing softsensor Mooney viscosity ALCEKL
Yan-chen Gao Jun Ji Hai-qing Wang Ping Li
State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control, Zhejiang University Hangzhou, P.R.China
国际会议
厦门
英文
470-474
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）