Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 1
110-114

BN parameter learning algorithm based on dynamic weighted transfer learning

Guo Wenqiang1a
Xu Cheng1b
Xiao Qinkun2
Li Mengran1b
1. a. School of Electronic Information & Artificial Intelligence, b. School of Electrical & Control Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
2. School of Electronic Information Engineering, Xi'an University of Technology, Xi'an 710021, China
DOI: 10.19734/j.issn.1001-3695.2019.10.0600

Abstract

To solve the problem of Bayesian network(BN) parameter estimation accuracy under small dataset conditions, this paper proposed a parameter dynamic weighted transfer learning algorithm(DWTL) based on varying weight transfer learning. Firstly, the presented algorithm used MAP and the MLE method to learn the initial parameters of the target domain and the parameters of each source domain. Then, it obtained the source weight factors of the source domain by the different data source contributions. Based on the sample statistic, this method helped to obtain the final target parameters by fusing the data size threshold values, the balance coefficients for the target initial parameters with the source domain parameters. The experimental results show that under the condition of the small data set, the learning accuracy of DWTL algorithm is better than MLE algorithm, MAP algorithm and traditional transfer learning algorithm(LP). Under the condition of sufficient data set, the learning accuracy of DWTL algorithm approaches the classical MLE algorithm, and verifies the correctness of the algorithm. Moreover, it demonstrates successful application to real-world bearing fault diagnosis case studies. Comparing with the LP algorithm, the DWTL algorithm achieves about 10% enhancement for the average diagnosis precision.

Foundation Support

国家自然科学基金资助项目(61271362,62071366)
陕西省科技厅重点研发计划资助项目(2020SF-286)
陕西省科技厅自然科学基金资助项目(2017JM6057)
陕西省教育厅产业化研究项目(18JC003)
西安市科技计划资助项目(2019216514GXRC001CG002GXYD1.1)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.10.0600
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 1
Section: Algorithm Research & Explore
Pages: 110-114
Serial Number: 1001-3695(2021)01-022-0110-05

Publish History

[2021-01-05] Printed Article

Cite This Article

郭文强, 徐成, 肖秦琨, 等. 基于变权重迁移学习的BN参数学习算法 [J]. 计算机应用研究, 2021, 38 (1): 110-114. (Guo Wenqiang, Xu Cheng, Xiao Qinkun, et al. BN parameter learning algorithm based on dynamic weighted transfer learning [J]. Application Research of Computers, 2021, 38 (1): 110-114. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)