Abstract
According to the secondary variables acquired from industrial processes, a Least Squares Support Vector Machine (LSSVM) based model is proposed for the primary variable soft sensing. The Rough Sets Theory is firstly employed to compress values and attributes of the secondary variables. Then the LSSVM is delivered for the primary variable nonlinear estimating. The method is applied for the vacuum oil purification machine. The moisture content in oil, a hard-to-be-measured primary variable, is computed from the soft sensor model. The result shows that the proposed method features a faster and more precise approximation ability.
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