2023
DOI: 10.1177/01423312231181379
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A data-driven soft-sensing approach using probabilistic latent variable model with functional data framework

Abstract: Functional principal component analysis (FPCA) and functional partial least squares (FPLS) are two mainstream functional data analysis (FDA) methods, which have been commonly used to extract deep information hidden in the original data space. However, the process data always contain random noise, which affects the performance of FDA models. To overcome this issue, two functional probabilistic latent variable models (FPLVMs), including functional probabilistic principal component analysis (FPPCA) and functional… Show more

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