TY - GEN
T1 - RBF neural network controller based on OLSSVR
AU - Ucak, Kemal
AU - Oke, Gulay
PY - 2013
Y1 - 2013
N2 - In this paper, a predictive adaptation method based on Online Least Square Support Vector Regression (OLSVR) for a RBF controller has been proposed. System Jacobian is approximated via Online LSSVR model of the system to tune RBF controller. The parameters of the controller have been tuned depending on K-step ahead future behavior of the system to provide adaptation ability to the controller under changing conditions. Levenberg Marquard algorithm is utilized as learning algorithm for controller parameters. The proposed method has been evaluated by simulations carried out on a magnetic levitation system, and the results show that the control performance has been improved.
AB - In this paper, a predictive adaptation method based on Online Least Square Support Vector Regression (OLSVR) for a RBF controller has been proposed. System Jacobian is approximated via Online LSSVR model of the system to tune RBF controller. The parameters of the controller have been tuned depending on K-step ahead future behavior of the system to provide adaptation ability to the controller under changing conditions. Levenberg Marquard algorithm is utilized as learning algorithm for controller parameters. The proposed method has been evaluated by simulations carried out on a magnetic levitation system, and the results show that the control performance has been improved.
KW - Levenberg Marquard
KW - Magnetic Levitation
KW - Online Support Vector Regression
KW - RBF Neural Network Controller
UR - https://www.scopus.com/pages/publications/84886510611
U2 - 10.1109/ASCC.2013.6606293
DO - 10.1109/ASCC.2013.6606293
M3 - Conference contribution
AN - SCOPUS:84886510611
SN - 9781467357692
T3 - 2013 9th Asian Control Conference, ASCC 2013
BT - 2013 9th Asian Control Conference, ASCC 2013
T2 - 2013 9th Asian Control Conference, ASCC 2013
Y2 - 23 June 2013 through 26 June 2013
ER -