Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms

doi:10.1007/s11704-009-0020-8

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	Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms
	Yu, Lean 1; Wang, Shouyang 1; Lai, Kin Keung 2
	2009-06-01
发表期刊	FRONTIERS OF COMPUTER SCIENCE IN CHINA
ISSN	1673-7350
卷号	3 期号:2 页码:167-176
摘要	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 conjugate-gradient 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 sigma limits theory." Using the adaptive smoothing momentum terms, the improved BP learning algorithm can make the network training and convergence process faster, and the network's generalization performance stronger than the standard BP learning algorithm can do. In order to verify the effectiveness of the proposed BP learning algorithm, three typical foreign exchange rates, British pound (GBP), Euro (EUR), and Japanese yen (JPY), are chosen as the forecasting targets for illustration purpose. Experimental results from homogeneous algorithm comparisons reveal that the proposed BP learning algorithm outperforms the other comparable BP algorithms in performance and convergence rate. Furthermore, empirical results from heterogeneous model comparisons also show the effectiveness of the proposed BP learning algorithm.
关键词	back-propagation neural network adaptive smoothing momentum heuristic method foreign exchange rates forecasting
DOI	10.1007/s11704-009-0020-8
语种	英语
资助项目	National Natural Science Foundation of China[70601029] ; National Natural Science Foundation of China[70221001] ; Chinese Academy of Sciences ; City University of Hong Kong
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000207971000004
出版者	HIGHER EDUCATION PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/7266
专题	中国科学院数学与系统科学研究院
通讯作者	Yu, Lean
作者单位	1.Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.City Univ Hong Kong, Dept Management Sci, Hong Kong, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yu, Lean,Wang, Shouyang,Lai, Kin Keung. Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms[J]. FRONTIERS OF COMPUTER SCIENCE IN CHINA,2009,3(2):167-176.
APA	Yu, Lean,Wang, Shouyang,&Lai, Kin Keung.(2009).Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms.FRONTIERS OF COMPUTER SCIENCE IN CHINA,3(2),167-176.
MLA	Yu, Lean,et al."Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms".FRONTIERS OF COMPUTER SCIENCE IN CHINA 3.2(2009):167-176.