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作者:

Duan, Haoshan (Duan, Haoshan.) | Meng, Xi (Meng, Xi.) | Tang, Jian (Tang, Jian.) (学者:汤健) | Qiao, Junfei (Qiao, Junfei.)

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EI Scopus SCIE

摘要:

Establishing an accurate model of dynamic systems poses a challenge for complex industrial processes. Due to the ability to handle complex tasks, modular neural networks (MNN) have been widely applied to industrial process modeling. However, the phenomenon of domain drift caused by operating conditions may lead to a cold start of the model, which affects the performance of MNN. For this reason, a multisource transfer learning-based MNN (MSTL-MNN) is proposed in this study. First, the knowledge-driven transfer learning process is performed with domain similarity evaluation, knowledge extraction, and fusion, aiming to form an initial subnetwork in the target domain. Then, the positive transfer process of effective knowledge can avoid the cold start problem of MNN. Second, during the data-driven fine-tuning process, a regularized self-organizing long short-term memory algorithm is designed to fine-tune the structure and parameters of the initial subnetwork, which can improve the prediction performance of MNN. Meanwhile, relevant theoretical analysis is given to ensure the feasibility of MSTL-MNN. Finally, the effectiveness of the proposed method is confirmed by two benchmark simulations and a real industrial dataset of a municipal solid waste incineration process. Experimental results demonstrate the merits of MSTL-MNN for industrial applications.

关键词:

Computational modeling Dynamic system Task analysis multisource transfer learning Mathematical models modular neural network (MNN) Multi-layer neural network Prediction algorithms Dynamical systems Neurons long short-term memory (LSTM)

作者机构:

  • [ 1 ] [Duan, Haoshan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Meng, Xi]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Tang, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Duan, Haoshan]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 6 ] [Meng, Xi]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 7 ] [Tang, Jian]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 8 ] [Qiao, Junfei]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 9 ] [Duan, Haoshan]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 10 ] [Meng, Xi]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 11 ] [Tang, Jian]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 12 ] [Qiao, Junfei]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China

通讯作者信息:

  • [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

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来源 :

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

ISSN: 1551-3203

年份: 2024

期: 5

卷: 20

页码: 7173-7182

1 2 . 3 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 1

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

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