Titlebook: Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research; Chao Shang Book 2018 Springer Nature Singa

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Enhanced Dynamic PLS with Temporal Smoothness for Soft Sensing,ng process dynamics appropriately. Hence, a series of limitations in practice are incurred, such as sensitivity to temporal noises and inadequate descriptions to process dynamics. Because of these concerns, static models have been extended to dynamic counterparts such dynamic partial least squares (

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Nonlinear Dynamic Soft Sensing Based on Bayesian Inference,e whole model has a Wiener structure, in which nonlinearity and dynamics are described separately. In addition, a novel four-level Bayesian framework is developed to probabilistically illustrate and iteratively optimize the proposed model, which helps alleviating the over-fitting phenomenon automati

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2190-5053 zing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industr978-981-13-3889-2978-981-10-6677-1Series ISSN 2190-5053 Series E-ISSN 2190-5061

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Enhanced Dynamic PLS with Temporal Smoothness for Soft Sensing,which is used as a valid prior knowledge. In this manner, abrupt changes in model dynamics are properly penalized and the DPLS-based soft sensors enjoy better generalizations and interpretations. A numerical example and the Tennessee Eastman process case study are provided to show the feasibility as

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Rohstoffwirtschaftliche Entwicklungshilfewhich is used as a valid prior knowledge. In this manner, abrupt changes in model dynamics are properly penalized and the DPLS-based soft sensors enjoy better generalizations and interpretations. A numerical example and the Tennessee Eastman process case study are provided to show the feasibility as