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

Content Provider	IEEE Xplore Digital Library
Author	Guoyu Zuo Xingdi Yuan Daoxiong Gong Chao Liang
Copyright Year	2011
Description	Author affiliation: School of Electronic Information and Control Engineering, Beijing University of Technology, 100124, China (Guoyu Zuo; Xingdi Yuan; Daoxiong Gong; Chao Liang)
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.
Starting Page	626
Ending Page	630
File Size	446755
Page Count	5
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022210
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning defense strategy BP neural network Games Brain modeling Educational institutions robot soccer Data models Weighted Mean Distance Model Joining processes Robots
Content Type	Text
Resource Type	Article

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