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作者:

Li, Wei (Li, Wei.) | Qiao, Junfei (Qiao, Junfei.) (学者:乔俊飞) | Zeng, Xiao-Jun (Zeng, Xiao-Jun.)

收录:

EI Scopus SCIE

摘要:

This article proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online, but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.

关键词:

online learning Merging Neurons Fuzzy neural networks (FNNs) self-learning Data models Firing Computational modeling Real-time systems Fuzzy neural networks recursive least square (RLS)

作者机构:

  • [ 1 ] [Li, Wei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Li, Wei]Fujian Univ Technol, Sch Comp Sci & Math, Fuzhou 350118, Peoples R China
  • [ 4 ] [Zeng, Xiao-Jun]Univ Manchester, Dept Comp Sci, Manchester M13 9PL, Lancs, England

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来源 :

IEEE TRANSACTIONS ON FUZZY SYSTEMS

ISSN: 1063-6706

年份: 2022

期: 3

卷: 30

页码: 649-662

1 1 . 9

JCR@2022

1 1 . 9 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:49

JCR分区:1

中科院分区:1

被引次数:

WoS核心集被引频次: 13

SCOPUS被引频次: 17

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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