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作者:

Han, Honggui (Han, Honggui.) (学者:韩红桂) | Wang, Lidan (Wang, Lidan.) | Qiao, Junfei (Qiao, Junfei.) (学者:乔俊飞) | Feng, Gang (Feng, Gang.)

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EI Scopus

摘要:

In this paper, a spiking growing algorithm (SGA) is proposed for optimizing the structure of radial basis function (RBF) neural network. Inspired by the synchronous behavior of spiking neurons, the spiking strength (ss) of the hidden neurons is defined as the criteria of SGA, which investigates a new way to simulate the connections between hidden and output neurons of RBF neural network. This SGA-based RBF (SGA-RBF) neural network can self-organize the hidden neurons online, to achieve the appropriate network efficiency. Meanwhile, to ensure the accuracy of SGA-RBF neural network, the structure-adjusting and parameters-training phases are performed simultaneously. Simulation results demonstrate that the proposed method can obtain a higher precision in comparison with some other existing methods. © 2014 IEEE.

关键词:

Functions Neurons Nonlinear systems Radial basis function networks

作者机构:

  • [ 1 ] [Han, Honggui]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
  • [ 2 ] [Han, Honggui]Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong
  • [ 3 ] [Wang, Lidan]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
  • [ 4 ] [Qiao, Junfei]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
  • [ 5 ] [Feng, Gang]Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong
  • [ 6 ] [Feng, Gang]Nanjing University of Science and Technology, Nanjing; 210094, China

通讯作者信息:

  • 韩红桂

    [han, honggui]department of mechanical and biomedical engineering, city university of hong kong, kowloon, hong kong;;[han, honggui]college of electronic and control engineering, beijing university of technology, beijing, china

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年份: 2014

页码: 3775-3782

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 4

ESI高被引论文在榜: 0 展开所有

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