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Soft-sensing Design Based on Semiclosed-loop Framework
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DOI：10.1016/S1004-9541(12)60610-7.png)
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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.
Keyword:
soft-sensing
neural network
semiclosed-loop framework
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3.7
论文数:
5.2K
被引数:
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