A Spiking-based mechanism for self-organizing RBF neural networks - 文章详情页

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.) 展开

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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 Radial basis function networks Nonlinear systems Neurons

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

查看成果更多字段

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Interval model for predicting effluent quality variables of wastewater treatment plants
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RBF Neural network based on ART neural network
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来源：

年份： 2014

页码： 3775-3782

语种：英文

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SCOPUS被引频次： 5

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归属院系：

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环境与生命学部生命科学与生物工程学院

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