Predictive models with endogenous variables for quality control in customized scenarios affected by multiple setups

Korzenowski, André Luís; Anzanello, Michel J.; Portugal, Marcelo Savino; Caten, Carla ten

doi:10.1016/j.cie.2013.04.011

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The numbers of the three nodes in the hidden layer of AEs in SAE Figure 9 Flow chart of the atmospheric column. (5) 0.075694 x (9) 0.053006 x (13) 0.072433 x (2) 0.069409 x (6) 0.103963 x (10) 0.054964 x (14) 0.054684 x (3) 0 x (7) 0.050637 x (11) 0.084609 x (15) 0.064075 x (4) 0 x (8) 0.034854 x (12) 0.085078 x (16) 0.124650 are 9, 6, and 4. Figure 12 gives the absolute error trend along with the test sample number in the naphtha dry point dataset by our proposed method.…”

Section: Experiments On the Naphtha Dry Point Datasetmentioning

confidence: 99%

“…A number of data-driven modeling methods are available, and these methods are mainly divided into two categories. One is based on multivariate statistical algorithms, including principal component regression (PCR) [5] and partial least-squares regression (PLS) [6], and the other is based on statistical machine learning algorithms, such as support vector regression (SVR) [7], genetic algorithm (GA) [8], and artificial neural network (ANN) [9]. Although these algorithms can be applied to various fields, some problems, such as those related to robustness and accuracy, still exist in the soft sensor modeling process.…”

Section: Introductionmentioning

confidence: 99%

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Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients

Wang¹,

Yan²

2019

IJCIS

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KeywordsSoft sensor Deep learning Stacked autoencoder (SAE) Maximal information coefficient (MIC) Modeling method A B S T R A C TThe key factors required to establish a precise soft sensor model for industrial processes include selection of variables affecting vital indicators from a large number of online measurement variables and elimination of the effects of unrelated disturbance variables. How to compress redundant information and retain the unique characteristic information contained by the selected variables is worthy of in-depth research. A novel soft sensor modeling method based on weighted maximal information coefficients (MICs) and a stacked autoencoder (SAE), hereinafter referred to as MICW-SAE, is proposed in this work. In our model, the MICs between each input and output variable are calculated and compared with the threshold before training each network in SAE. Then, input variables with low MICs are selected, and the average MIC index is calculated using other input variables. If the index is higher than the second threshold, the MIC of this specific variable is set to 0. Finally, the weights of all input variables are determined in accordance with the scale and placed into the loss function for training. The Boston house-price and naphtha dry point temperature datasets are used to prove the prediction ability of our model. Results demonstrate that MICW-SAE can enhance the output-related features of the input variables. Moreover, redundant information that can also be represented by other input variables are identified and excluded.

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