TY - GEN
T1 - Support vector network enhanced adaptive friction compensation
AU - Wang, G. L.
AU - Li, Y. F.
AU - Bi, D. X.
PY - 2006
Y1 - 2006
N2 - This paper explores the notation of support vector networks, a new paradigm of combining support vector regression (SVR) parametrization with adaptive neural mechanism, in friction compensation for servo-motion systems. The contribution of this work is twofold. The first is to develop an enhanced adaptive friction compensator via SVR parametrization; the second is to present an analysis that shows the evidences of the performance improvement and practical usefulness enhancement due to SVR parametrization. The experimental study was conducted to validate the proposed method. © 2006 IEEE.
AB - This paper explores the notation of support vector networks, a new paradigm of combining support vector regression (SVR) parametrization with adaptive neural mechanism, in friction compensation for servo-motion systems. The contribution of this work is twofold. The first is to develop an enhanced adaptive friction compensator via SVR parametrization; the second is to present an analysis that shows the evidences of the performance improvement and practical usefulness enhancement due to SVR parametrization. The experimental study was conducted to validate the proposed method. © 2006 IEEE.
UR - https://www.scopus.com/pages/publications/33845620441
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33845620441&origin=recordpage
U2 - 10.1109/ROBOT.2006.1642267
DO - 10.1109/ROBOT.2006.1642267
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780395069
SN - 9780780395060
VL - 2006
SP - 3699
EP - 3704
BT - Proceedings - IEEE International Conference on Robotics and Automation
T2 - 2006 IEEE International Conference on Robotics and Automation, ICRA 2006
Y2 - 15 May 2006 through 19 May 2006
ER -