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- W2160614705 abstract "In this paper, a new fuzzy adaptive local modeling method based on local learning and weighted least squares support vector machine (LS-SVM) is proposed by building fuzzy membership model for the training data. Just as LSSVM, local LS-SVM is also sensitive to outliers or noises. A proper fuzzy membership model based on support vector data description (SVDD) is proposed to deal with the problem. Fuzzy membership value to each input sample is confirmed according to it’s distance to the center of smallest nclosing hyper sphere determined by SVDD. The proposed local weighted LS-SVM is applied to predict the concentration of 4-Carboxybenzaldchydc (4-CBA) in purified terephthalic acid (PTA) oxidation process. Results indicate that the proposed method actually reduces the effect of outliers and its accuracy is improved." @default.
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- W2160614705 date "2010-12-01" @default.
- W2160614705 modified "2023-09-24" @default.
- W2160614705 title "Concentration Prediction of 4-CBA Based on Local Weighted LS-SVM" @default.
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- W2160614705 doi "https://doi.org/10.1109/icdma.2010.233" @default.
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