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政大機構典藏 > 社會科學學院 > 經濟學系 > 專書/專書篇章 > Item 140.119/122238

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/122238

Title:	Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data
Authors:	陳樹衡 Chen, Shu-Heng Ni, C.-C.
Contributors:	經濟學系學
Date:	1998
Issue Date:	2019-01-31 13:44:43 (UTC+8)
Abstract:	In this paper, the stock index S&P 500 is used to test the predicting performance of genetic programming (GP) and genetic programming neural networks (GPNN). While both GP and GPNN are considered universalapproximators, in this practical financial application, they perform differently. GPNN seemed to suffer the overlearning problem more seriously than GP; the latter outdid the former in all the simulations.
Relation:	Artificial Neural Nets and Genetic Algorithms pp 397-400
Data Type:	book/chapter
DOI 連結:	https://doi.org/10.1007/978-3-7091-6492-1_87
DOI:	10.1007/978-3-7091-6492-1_87
Appears in Collections:	[經濟學系] 專書/專書篇章

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6492-1_87.pdf		675Kb	Adobe PDF2	308	View/Open

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