Presentation 1996/5/24
Performance evaluation of two algorithms for learning in ANN based on a real financial prediction application
Yadira Solano, Hiroaki IKEDA,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) The purpose of this study is to present results of forecast of ranges for yen to US dollar exchange rate fluctuation in order to evaluate the performance of two algorithms: the original backpropagation (OBP), which is the most widely used algorithm, and the second algorithm (NBP), which is a modification to the first one previously proposed by the authors. The set of data consisted of economic and financial values that have already been calculated by the Bank of Japan and the Japanese Ministry of Planning and Finance. This data was available though the Nikkei Data Service and stretched from January, 1986, to the end of December, 1992. The results obtained show that NBP performs better than OBP since the former speeds up convergence time to a given error value.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) neural networks / backpropagation / learning / financial forecasting.
Paper # AI96-1
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Conference Information
Committee AI
Conference Date 1996/5/24(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Performance evaluation of two algorithms for learning in ANN based on a real financial prediction application
Sub Title (in English)
Keyword(1) neural networks
Keyword(2) backpropagation
Keyword(3) learning
Keyword(4) financial forecasting.
1st Author's Name Yadira Solano
1st Author's Affiliation Faculty of Natural Sciences, Graduate School of Science and Technology, Chiba University()
2nd Author's Name Hiroaki IKEDA
2nd Author's Affiliation Faculty of Natural Sciences, Graduate School of Science and Technology, Chiba University
Date 1996/5/24
Paper # AI96-1
Volume (vol) vol.96
Number (no) 77
Page pp.pp.-
#Pages 8
Date of Issue