TY - GEN
T1 - A Hybrid algorithm of weight evolution and generalized back-propagation for finding global minimum
AU - Ng, Sin-Chun
AU - Leung, Shu-Hung
AU - Luk, Andrew
PY - 1999/7
Y1 - 1999/7
N2 - The conventional back-propagation algorithm will always get stuck into local minima and converge very slowly. Other fast algorithms can increase the convergence speed however they still converge to local minima. We introduce a new hybrid algorithm with the use of weight evolution into the generalized back-propagation method. The hybrid algorithm further improve the convergence rate and the global convergence capability, it ensures the convergence to a global minimum in a compact region of a weight vector space. © 1999 IEEE
AB - The conventional back-propagation algorithm will always get stuck into local minima and converge very slowly. Other fast algorithms can increase the convergence speed however they still converge to local minima. We introduce a new hybrid algorithm with the use of weight evolution into the generalized back-propagation method. The hybrid algorithm further improve the convergence rate and the global convergence capability, it ensures the convergence to a global minimum in a compact region of a weight vector space. © 1999 IEEE
UR - https://www.scopus.com/pages/publications/0033307738
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0033307738&origin=recordpage
U2 - 10.1109/IJCNN.1999.830806
DO - 10.1109/IJCNN.1999.830806
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0-7803-5529-6
VL - 6
SP - 4037
EP - 4042
BT - International Joint Conference on Neural Networks, WASHINGTON, DC, JULY 10-16, 1999
PB - IEEE
T2 - 1999 International Joint Conference on Neural Networks (IJCNN'99)
Y2 - 10 July 1999 through 16 July 1999
ER -