An adaptive BP algorithm with optimal learning rates and directional error correction for foreign exchange market trend prediction

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	An adaptive BP algorithm with optimal learning rates and directional error correction for foreign exchange market trend prediction
	Yu, Lean ; Wang, Shouyang; Lai, Kin Keung
	2006
发表期刊	ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS
ISSN	0302-9743
卷号	3973 页码:498-503
摘要	In this study, a novel adaptive BP learning algorithm with optimal learning rates and directional error correction is proposed. In this algorithm, the optimal adaptive learning rates are used to adjust the weights in an adaptive and dynamical way. Meantime, the directional errors are fed back to learning process to increase the directional accuracy. For illustration and testing purposes the proposed algorithm is applied to the foreign exchange market trend prediction.
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000239485300073
出版者	SPRINGER-VERLAG BERLIN
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/2636
专题	系统科学研究所
通讯作者	Yu, Lean
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100080, Peoples R China 2.Hunan Univ, Coll Business Adm, Changsha 410082, Peoples R China 3.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yu, Lean,Wang, Shouyang,Lai, Kin Keung. An adaptive BP algorithm with optimal learning rates and directional error correction for foreign exchange market trend prediction[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS,2006,3973:498-503.
APA	Yu, Lean,Wang, Shouyang,&Lai, Kin Keung.(2006).An adaptive BP algorithm with optimal learning rates and directional error correction for foreign exchange market trend prediction.ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS,3973,498-503.
MLA	Yu, Lean,et al."An adaptive BP algorithm with optimal learning rates and directional error correction for foreign exchange market trend prediction".ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS 3973(2006):498-503.