.png){kind=icon}
Return
Soft-sensing Design Based on Semiclosed-loop Framework
.png){kind=icon}
PRE
AI
DOI：10.1016/S1004-9541(12)60610-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Soft-sensing is widely used in industrial applications. The traditional soft-sensing structure is open-loop without correction mechanism. If the working condition is changed or there is unknown disturbance, the forecast result of soft-sensing model may be incorrect. In order to obtain accurate values, it is necessary to carry out online correction. In this paper, a semiclosed-loop framework (SLF) is proposed to establish a soft-sensing approach, which estimates the input variables in the next moment by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and robustness than other open-loop models.
Keywords:
soft-sensing
neural network
semiclosed-loop framework
Journal
IF:
3.7
Papers:
5.3K
Citations:
1.1W
Organization
Cited Papers
ShareSaveShareSaveShareSaveHigh-density circulating fluidized bed gasifier for advanced IGCC/IGFC-Advantages and challenges.png)
PARTICUOLOGY
PARTICUOLOGYIF4.3
PREAI
ShareSaveShareSave
View more