%A Lean YU, Shouyang WANG, Kin Keung LAI %T Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms %0 Journal Article %D 2009 %J Front. Comput. Sci. %J Frontiers of Computer Science %@ 2095-2228 %R 10.1007/s11704-009-0020-8 %P 167-176 %V 3 %N 2 %U {https://journal.hep.com.cn/fcs/EN/10.1007/s11704-009-0020-8 %8 2009-06-05 %X
The slow convergence of back-propagation neural network (BPNN) has become a challenge in data-mining and knowledge discovery applications due to the drawbacks of the gradient descent (GD) optimization method, which is widely adopted in BPNN learning. To solve this problem, some standard optimization techniques such as conjugategradient and Newton method have been proposed to improve the convergence rate of BP learning algorithm. This paper presents a heuristic method that adds an adaptive smoothing momentum term to original BP learning algorithm to speedup the convergence. In this improved BP learning algorithm, adaptive smoothing technique is used to adjust the momentums of weight updating formula automatically in terms of “3