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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
521-526

Semi-supervised dynamic deep fusion neural network based soft sensor

Guo Xiaoping
Chong Jialin
Li Yuan
College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0276

Abstract

The semi-supervised deep neural network modeling method has been widely applied in soft sensor, but the network based on hierarchical training only excavates the effective information of each input layer in the feature extraction process, ignoring the loss of effective information of the original input and accumulating it layer by layer, resulting in low accuracy of feature representation of the original input. In addition, the lack of spatiotemporal information related to the mining process can also lead to model performance degradation. This paper proposed a semi-supervised dynamics deep fusion neural network(SS-DDFNN) method. This method reconstructed the original input data and predicted quality variables at each layer of the feature extraction network. By using the reconstructed original input error in pre-training loss, it reduced the loss of effective information from the original input. Simultaneously it incorporated attention mechanism and t-distribution random neighborhood embedding to extract spatiotemporal related information, and established a gated neural network quality prediction model using extracted features. The experimental results show that compared to the SAE, GSTAE, and SIAE models, the proposed method has improved prediction accuracy by 2.8%, 1.1%, and 0.9% in the case of a debutanizer, respectively. In the industrial polyethylene production case, it has increased by 2.7%, 1.0%, and 0.7% respectively. The experimental results show that the proposed method is effective.

Foundation Support

国家自然科学基金资助项目(62273242)
辽宁省教育厅科学研究一般项目(LJ2020021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0276
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: System Development & Application
Pages: 521-526
Serial Number: 1001-3695(2024)02-030-0521-06

Publish History

[2023-08-31] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

郭小萍, 种佳林, 李元. 基于半监督动态深度融合神经网络的软测量 [J]. 计算机应用研究, 2024, 41 (2): 521-526. (Guo Xiaoping, Chong Jialin, Li Yuan. Semi-supervised dynamic deep fusion neural network based soft sensor [J]. Application Research of Computers, 2024, 41 (2): 521-526. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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