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- W2896098101 abstract "Most regression approaches, such as principal component analysis (PCA), are based on an assumption that the process data follow a Gaussian distribution. However, the process data usually dissatisfy that assumption. Thus, the locally weighted standardization (LWS) method is employed for transforming data into an approximate Gaussian distribution. Furthermore, the LWS based subspace PCA ensemble modeling method is developed. The subspace PCA can select important variables in each subspace for ensemble modeling. As a result, the proposed method gives a weaker assumption constrain and a better regression performance. The effectiveness of this approach is testified by two study cases." @default.
- W2896098101 created "2018-10-26" @default.
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- W2896098101 date "2018-01-01" @default.
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- W2896098101 title "LWS based PCA subspace ensemble model for soft sensor development" @default.
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- W2896098101 doi "https://doi.org/10.1016/j.ifacol.2018.09.350" @default.
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