TY - GEN
T1 - Identification of quasi-ARX neurofuzzy model by using SVR-based approach with input selection
AU - Cheng, Yu
AU - Wang, Lan
AU - Zeng, Jing
AU - Hu, Jinglu
PY - 2011
Y1 - 2011
N2 - Quasi-ARX neurofuzzy (Q-ARX-NF) models have shown great approximation ability and usefulness in nonlinear system identification and control. However, the incorporated neurofuzzy networks suffer from the curse-of-dimensionality problem, which may result in high computational complexity and over-fitting. In this paper, support vector regressor (SVR) based identification approach is used to reduce computational complexity with the help of transforming the original problem into Lagrange space, which is only sensitive to the number of data samples. Furthermore, to improve the generalization capability, a parsimonious model structure is obtained by eliminating insignificant input variables for the incorporated neurofuzzy network, which is implemented by genetic algorithm (GA) based input selection method with a novel fitness evaluation function. Two numerical simulations are tested to show the effectiveness of the proposed method.
AB - Quasi-ARX neurofuzzy (Q-ARX-NF) models have shown great approximation ability and usefulness in nonlinear system identification and control. However, the incorporated neurofuzzy networks suffer from the curse-of-dimensionality problem, which may result in high computational complexity and over-fitting. In this paper, support vector regressor (SVR) based identification approach is used to reduce computational complexity with the help of transforming the original problem into Lagrange space, which is only sensitive to the number of data samples. Furthermore, to improve the generalization capability, a parsimonious model structure is obtained by eliminating insignificant input variables for the incorporated neurofuzzy network, which is implemented by genetic algorithm (GA) based input selection method with a novel fitness evaluation function. Two numerical simulations are tested to show the effectiveness of the proposed method.
KW - Quasi-ARX neurofuzzy network
KW - SVR
KW - identification
KW - input selection
UR - http://www.scopus.com/inward/record.url?scp=83755195079&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83755195079&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2011.6083897
DO - 10.1109/ICSMC.2011.6083897
M3 - Conference contribution
AN - SCOPUS:83755195079
SN - 9781457706523
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 1585
EP - 1590
BT - 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
T2 - 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
Y2 - 9 October 2011 through 12 October 2011
ER -