Soft sensor based on eXtreme gradient boosting and bidirectional converted gates long short-term memory self-attention network

In this paper, a new soft sensor that combines eXtreme Gradient Boosting (Xgboost) decision trees and a bidirectional, converted gate long short-term memory (BiCG-LSTMs) self-attention (SEA) mechanism network is proposed. Xgboost is first utilized to select relevant input variables according to their importance. It then acts as an encoder to weigh the selected input variables based on their importance scores. The encoded input variables are normalized and then sent to the bidirectional converted gates LSTM (BiCG-LSTMs) to extract dynamic information hidden in the process data. The BiCG-LSTMs is designed to avoid multiple gates function, a characteristic of traditional LSTM units in bidirectional LSTM that consumes additional calculation time. Next, a regularization method by smoothing dynamic features based on self-attention weights is utilized to denoise and alleviate the overfitting of the regression once new features are added. In addition, self-attention takes into account the internal dependence of input variables regardless how far the distance between input variables. Finally, the effectiveness of the proposed Xgboost-BiCG-LSTM-SEA soft sensor framework is demonstrated by an application to the prediction of melt intrinsic viscosity of the polyester polymerization process. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a new soft sensor framework that combines Xgboost decision trees and BiCG-LSTMs with self-attention mechanism network for predicting the melt intrinsic viscosity of polyester polymerization process. The framework selects relevant input variables, weights them, extracts hidden dynamic information, smoothes dynamic features and reduces overfitting. This approach effectively demonstrates the prediction accuracy in the application scenario.