DocumentCode
2913140
Title
Evolutionary trained radial basis function networks for robot control
Author
Vidnerová, Petra ; Slusny, S. ; Neruda, Roman
Author_Institution
Inst. of Comput. Sci., Acad. of Sci. of the Czech Republic, Prague
fYear
2008
fDate
17-20 Dec. 2008
Firstpage
833
Lastpage
838
Abstract
An emergence of intelligent behaviour within a simple robotic agent is studied in this paper. The radial basis function neural network is used as the control mechanism of the robot. Evolutionary algorithm is used to train the agent to perform several tasks. A comparison to multilayer perceptron neural networks and reinforcement learning is made and the results are discussed.
Keywords
evolutionary computation; learning (artificial intelligence); neurocontrollers; radial basis function networks; robots; evolutionary trained radial basis function networks; multilayer perceptron neural networks; reinforcement learning; robot control; Erbium; Evolutionary computation; Intelligent robots; Mobile robots; Multilayer perceptrons; Neural networks; Radial basis function networks; Robot control; Robot sensing systems; Robotics and automation; RBF networks; evolutionary robotics; genetic algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Control, Automation, Robotics and Vision, 2008. ICARCV 2008. 10th International Conference on
Conference_Location
Hanoi
Print_ISBN
978-1-4244-2286-9
Electronic_ISBN
978-1-4244-2287-6
Type
conf
DOI
10.1109/ICARCV.2008.4795625
Filename
4795625
Link To Document