Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection

Yiqin Shao; Jiale Tang; Jun Liu; Lixin Han; Shijian Dong

doi:10.1021/acsomega.3c06263

Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection

ACS Omega. 2023 Dec 7;8(50):47798-47811. doi: 10.1021/acsomega.3c06263. eCollection 2023 Dec 19.

Authors

Yiqin Shao¹, Jiale Tang^{2

3}, Jun Liu^{2

3}, Lixin Han^{2

3}, Shijian Dong^{2

3}

Affiliations

¹ Key Laboratory of Intelligent Textile and Flexible Interconnection of Zhejiang Province,College of Textiles Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
² Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China.
³ School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.

Abstract

Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.