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作者:

Yang, Yanxia (Yang, Yanxia.) | Wang, Pu (Wang, Pu.) | Gao, Xuejin (Gao, Xuejin.) | Gao, Huihui (Gao, Huihui.) | Qi, Zeyang (Qi, Zeyang.)

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EI Scopus

摘要:

Radial basis function neural network (RBFNN) has been widely used in industrial process modeling because of its strong approximation ability. However, many existing modeling methods aim at accuracy, but ignore the stability of mode. Therefore, this paper proposes a parameter optimization method of RBF neural network based on modified Levenberg-Marquardt (MLM-RBFNN) to ensure the stability of the network. Firstly, a typical sample mechanism with variance reduction is proposed, which can reduce the error of gradient estimation and use accurate gradient information to guide learning. Secondly, a modified LM optimization algorithm is proposed to optimize the parameters, which not only improve the convergence speed of the network, but also ensure the stability of the model. Finally, a multi-step updating rule based on a typical sample and a single sample is designed, which effectively reduces the sample bias introduced by a single sample. In order to prove the advantages of the MLM-RBFNN method proposed in this paper, experiments are carried out on three benchmark data sets and a practical wastewater treatment process application problem and compared with several existing methods. The results show that the proposed MLM-RBFNN method has good performance in both learning speed and stability. © 2022 - IOS Press. All rights reserved.

关键词:

Radial basis function networks Wastewater treatment Learning algorithms Benchmarking E-learning Learning systems Approximation algorithms Functions Stability

作者机构:

  • [ 1 ] [Yang, Yanxia]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Yang, Yanxia]Engineering Research Center of Digital Community, Ministry of Education, Beijing, China
  • [ 3 ] [Wang, Pu]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 4 ] [Wang, Pu]Engineering Research Center of Digital Community, Ministry of Education, Beijing, China
  • [ 5 ] [Gao, Xuejin]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 6 ] [Gao, Xuejin]Engineering Research Center of Digital Community, Ministry of Education, Beijing, China
  • [ 7 ] [Gao, Huihui]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 8 ] [Gao, Huihui]Engineering Research Center of Digital Community, Ministry of Education, Beijing, China
  • [ 9 ] [Qi, Zeyang]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 10 ] [Qi, Zeyang]Engineering Research Center of Digital Community, Ministry of Education, Beijing, China

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来源 :

Journal of Computational Methods in Sciences and Engineering

ISSN: 1472-7978

年份: 2022

期: 5

卷: 22

页码: 1597-1619

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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近30日浏览量: 2

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