TY - GEN
T1 - On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network
AU - Alizadeh, Tohid
AU - Salahshoor, Karim
AU - Jafari, Mohammad Reza
AU - Alizadeh, Abdullah
AU - Gholami, Mehdi
PY - 2007
Y1 - 2007
N2 - This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system.
AB - This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system.
UR - https://www.scopus.com/pages/publications/47849119074
UR - https://www.scopus.com/pages/publications/47849119074#tab=citedBy
U2 - 10.1109/EFTA.2007.4416777
DO - 10.1109/EFTA.2007.4416777
M3 - Conference contribution
AN - SCOPUS:47849119074
SN - 1424408261
SN - 9781424408269
T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
SP - 257
EP - 264
BT - 12th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2007 Proceedings
T2 - 12th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2007
Y2 - 25 September 2007 through 28 September 2007
ER -