A novel pruning algorithm for self-organizing neural network - Details

Author：

Honggui, Han (Honggui, Han.) (Scholars：韩红桂) | Junfei, Qiao (Junfei, Qiao.) (Scholars：乔俊飞)

Indexed by：

EI Scopus

Abstract：

In　this　paper,　a　novel　pruning　algorithm　is　proposed　for　self-organizing　the　feed-forward　neural　network　based　on　the　sensitivity　analysis,　named　novel　pruning　feed-forward　neural　network　(NP-FNN).　In　this　study,　the　number　of　hidden　neurons　is　determined　by　the　output＇s　sensitivity　to　the　hidden　nodes.　This　technique　determines　the　relevance　of　the　hidden　nodes　by　analyzing　the　Fourier　decomposition　of　the　variance.　Then　each　hidden　node　can　obtain　a　contribution　ratio.　The　connected　weights　of　the　hidden　nodes　with　small　ratio　will　be　set　as　zeros.　Therefore,　the　computational　cost　of　the　training　process　will　be　reduced　significantly.　It　is　clearly　shown　that　the　novel　pruning　algorithm　minimizes　the　complexity　of　the　final　feed-forward　neural　network.　Finally,　computer　simulation　results　are　carried　out　to　demonstrate　the　effectiveness　of　the　proposed　algorithm.　©2009　IEEE.

Keyword：

Computational complexity Sensitivity analysis Feedforward neural networks

Author Community：

[ 1 ] [Honggui, Han]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 2 ] [Junfei, Qiao]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China

Reprint Author's Address：

Email：

Rechard112@emails.biut.edu.cn

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