Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/122238| 題名: | Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data | 作者: | 陳樹衡 Chen, Shu-Heng Ni, C.-C. |
貢獻者: | 經濟學系學 | 日期: | 1998 | 上傳時間: | 31-Jan-2019 | 摘要: | 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. | 關聯: | Artificial Neural Nets and Genetic Algorithms pp 397-400 | 資料類型: | book/chapter | DOI: | https://doi.org/10.1007/978-3-7091-6492-1_87 |
| Appears in Collections: | 專書/專書篇章 |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 6492-1_87.pdf | 675.34 kB | Adobe PDF2 | View/Open |
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