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This paper proposes an adaptive soft sensing method based on selective ensemble of local partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear and time-varying chemical processes. To deal with the process nonlinearity, we partition the process state into local model regions upon which PLS models are constructed, through a statistical hypothesis testing based...
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