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作者:

Meng, Xi (Meng, Xi.) | Zhang, Yin (Zhang, Yin.) | Quan, Limin (Quan, Limin.) | Qiao, Junfei (Qiao, Junfei.)

收录:

EI Scopus SCIE

摘要:

Fuzzy neural networks (FNNs) integrating the advantages of fuzzy systems and neural networks are useful techniques for nonlinear system modeling. However, how to determine the structure and parameters to guarantee satisfactory modeling performance still remains a challenge. In this study, a self-organizing FNN with hybrid learning algorithm (SOFNN-HLA) is developed for nonlinear system modeling. First, a growing-and-pruning constructive scheme is proposed based on the network learning accuracy and the rule firing strength. New fuzzy rules can be developed with appropriate initial parameters based on the idea of an error-correction algorithm to improve the learning performance. Meanwhile, some redundant rules with low firing strength would be pruned to ensure a compact structure. Second, a hybrid learning algorithm combining an improved second-order algorithm and the least square method is developed for parameter adjustment. In this hybrid learning algorithm, linear parameters and nonlinear parameters are tackled separately to enhance the learning efficiency. Finally, the effectiveness of SOFNN-HLA is validated by two benchmark simulations and one real problem from wastewater treatment pro-cesses. The results show that the proposed SOFNN-HLA can achieve desirable generalization performance with a compact structure for nonlinear system modeling.

关键词:

Nonlinear system modeling Fuzzy neural network Hybrid learning algorithm Growing -and -pruning scheme

作者机构:

  • [ 1 ] [Meng, Xi]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Yin]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Meng, Xi]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 5 ] [Zhang, Yin]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 6 ] [Qiao, Junfei]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 7 ] [Meng, Xi]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 8 ] [Zhang, Yin]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 9 ] [Qiao, Junfei]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 10 ] [Quan, Limin]Qingdao Univ Technol, Sch Informat & Control Engn, Qingdao 266520, Peoples R China
  • [ 11 ] [Qiao, Junfei]Beijing Univ Technol, Beijing 100124, Peoples R China

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来源 :

INFORMATION SCIENCES

ISSN: 0020-0255

年份: 2023

卷: 642

8 . 1 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:19

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SCOPUS被引频次: 13

ESI高被引论文在榜: 0 展开所有

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