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Industrial process plants are instrumented with a large number of redundant sensors and the measured variables are often contaminated by random noises. Thus, it is significant to discover the general trends of data by latent variable models in the probabilistic framework before soft sensor modeling. However, traditional probabilistic latent variable models such as probabilistic principal component...
A novel multi-loop nonlinear Internal Model Control (IMC) strategy is developed for MIMO systems under the Partial Least Squares (PLS) framework, which automatically decomposes the MIMO process into several univariate systems in the latent space. The ARX-neural network (ARX-NN) model is incorporated in the PLS subspace and identified via optimizing two parameter sets so that the plant-model mismatch...
Process data exhibits both nonlinear and dynamic characteristics. A multivariable nonlinear dynamic modeling method is proposed by combining dynamic partial least squares (DPLS) algorithm and Hammerstein model. This method applies Hammerstein model to the DPLS inner regression. The outer PLS algorithm with ARX inputs is used to model the dynamics of the process, and it is also used to reduce the dimensionality...
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