Multiple Model Based Soft Sensor Development With Irregular/Missing Process Output Measurement
In this paper, nonlinear soft sensor development with irregular/missing output data is considered and a multiple model based modeling scheme is proposed for nonlinear processes. The efficiency of the proposed algorithm is demonstrated through several numerical simulation examples as well as the experimental data collected from a pilot-scale setup. It is shown through the comparison with the traditional missing data treatment methods in terms of the parameter estimation accuracy that, the developed soft sensors enjoy improved performance by employing the expectation-maximization (EM) algorithm in handling the missing process data and model varying problem.
Xing Jin Siyun Wang Biao Huang Fraser Forbes
Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada, T6G-2G6
国际会议
2011 International Symposium on Advanced Control of Industrial Processes(2011工业过程先进控制技术国际研讨会)
杭州
英文
293-298
2011-05-01（万方平台首次上网日期，不代表论文的发表时间）