Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients
- 10.2991/ijcis.d.190826.001How to use a DOI?
- Soft sensor; Deep learning; Stacked autoencoder (SAE); Maximal information coefficient (MIC); Modeling method
The 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.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Yanzhen Wang AU - Xuefeng Yan PY - 2019 DA - 2019/09/06 TI - Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients JO - International Journal of Computational Intelligence Systems SP - 1062 EP - 1074 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.190826.001 DO - 10.2991/ijcis.d.190826.001 ID - Wang2019 ER -