Abstract
In this paper, a modified Support Vector Regression (SVR) have been proposed as an empirical model for developing a soft sensor to estimate production quality of polymerization process which is highly nonlinear and that which has high dimension. This method is derived through the modification of the risk function of standard Support Vector Machine using the concept of Locally Weighted Regression. Case studies have shown that the proposed method shows a better performance over standard SVR that had been shown to be superior to traditional statistical learning machine in case of high dimension, sparse and nonlinear data.
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