Predicting the weights of a weighted mean distance model using BP NN for robot simulated soccer - Details

Author：

Zuo, Guoyu (Zuo, Guoyu.) (Scholars：左国玉) | Yuan, Xingdi (Yuan, Xingdi.) | Gong, Daoxiong (Gong, Daoxiong.) | Liang, Chao (Liang, Chao.)

Indexed by：

EI Scopus

Abstract：

To　improve　the　effectiveness　of　the　defense　strategy　of　Robocup　simulation　robot　soccer　team,　this　paper　proposes　a　BP　neural　network　(BP　NN)　method　to　predict　the　weights　of　weighted　mean　distance　model.　The　weighted　mean　distance　model　is　first　introduced　into　the　defense　strategy　of　one　robot　soccer　team.　A　BP　neural　network　model　is　then　built　to　modify　the　weights　of　the　weighted　mean　distance　model　to　realize　more　efficient　player-attaching　defense,　which　is　learned　and　decided　according　to　the　match　situations　in　the　court.　Experiment　results　show　that　the　weight　predication　method　for　the　weighted　mean　distance　model　using　BP　neural　network　can　effectively　improve　the　position　preciseness　and　reliability　of　the　defense　players　in　the　real　match　and　can　greatly　lessen　the　error　probability　in　robot　team＇s　defense　behaviors.　©　2011　IEEE.

Keyword：

Network security Neural networks Robots Robot learning Sports

Author Community：

[ 1 ] [Zuo, Guoyu]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Yuan, Xingdi]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Gong, Daoxiong]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Liang, Chao]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China