A design method for RBF neural network structure based on rough sets - Details

Author：

Chen, Shuangye (Chen, Shuangye.) | Chen, Yawei (Chen, Yawei.) | Zhang, Weijing (Zhang, Weijing.)

Indexed by：

EI Scopus

Abstract：

A　design　method　of　RBF　neural　network　construction　based　on　rough　sets　and　orthogonal　least　squares　(OLS)　is　proposed　in　this　paper.　First,　the　rough　sets　knowledge　expression　system　is　built　taking　use　of　the　large　number　of　system　sample　data.　The　impact　of　input　variables　on　output　variables　is　analyzed　through　computing　an　accuracy　measure　of　input　space　knowledge　on　output　space　knowledge　with　reducing　the　input　space　of　the　knowledge　expression　system,　System　order　and　the　input　layer　nodes　of　neural　network　can　be　decided　by　this　way.　Secondly,　the　hide　layer　nodes　and　the　weights　of　output　layer　of　the　neural　network　can　be　obtained　using　OLS　algorithm.　Finally,　the　research　of　simulations　on　a　nonlinear　system　are　carried　out　using　this　method　in　this　paper,　the　research　results　show　the　method　is　effective　and　feasible.　©　2010　IEEE.

Keyword：

Rough set theory Design Radial basis function networks Least squares approximations Multilayer neural networks

Author Community：

[ 1 ] [Chen, Shuangye]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
[ 2 ] [Chen, Yawei]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
[ 3 ] [Zhang, Weijing]College of Architecture Civil Engineering, Beijing University of Technology, Beijing, China