Soft sensing design based on a semiclosed-loop framework
Soft-sensing has been widely used in the case where the key variables are difficult to measure or can be measured but with a high cost. The traditional soft-sensing model is open-loop without correction mechanism. If the working condition is changed, the soft-sensing model which forecasts the following key variables will be not correct. In order to fetch the accurate value, it is necessary to carry out online correction. In this paper, a semiclosed-loop framework (SLF) is proposed to establish a soft-sensing approach. SLF approach estimates the input variables in the next moment by use of the prediction model and calibrate the output variables by use of the compensation model. The experimental results show that the proposed method has better prediction accuracy and robustness than other open-loop models in the problems of forecasting the sunspot numbers and flue gas oxygen content (FGOC).
soft-sensing neural network semiclosed-loop framework (SLF) sunspot numbers flue gas oxygen content (FGOC)
Qifeng Tang Dewei Li Yugeng Xi Debin Yin
Department of Automation, Shanghai Jiao Tong University,Key Laboratory of System Control and Informa Shanghai Xinhua Control Technology (Group) CO.,LTD, Shanghai 200241
国内会议
厦门
英文
1-8
2012-08-01（万方平台首次上网日期，不代表论文的发表时间）