DocumentCode
1685494
Title
An unsupervised learning of a layered network and its application to a motion acquisition
Author
Nishikawa, Ikuko ; Matsunaga, Kentaro
Author_Institution
Dept. of Comput. Sci., Ritsumeikan Univ., Shiga, Japan
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1667
Lastpage
1672
Abstract
An unsupervised learning for a layered network is applied to a motion acquisition of an autonomous agent. A basic algorithm is extended in the following two ways for temporal series learning. One is a temporal reward assignment, and the other is a network with temporal integration units. Several simulations show a successful learning of collision avoidance and a capture of both static and moving targets
Keywords
motion estimation; multilayer perceptrons; time series; unsupervised learning; autonomous agent; collision avoidance learning; layered network; motion acquisition; moving targets; multilayer neural network; static targets; temporal integration units; temporal reward assignment; temporal series learning; unsupervised learning; Application software; Collision avoidance; Computer science; Educational robots; Entropy; Neural networks; Neurons; Stochastic processes; Supervised learning; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007768
Filename
1007768
Link To Document