Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/122238| DC Field | Value | Language |
|---|---|---|
| dc.contributor | 經濟學系學 | zh_TW |
| dc.creator | 陳樹衡 | zh_TW |
| dc.creator | Chen, Shu-Heng | en_US |
| dc.creator | Ni, C.-C. | en_US |
| dc.date | 1998 | - |
| dc.date.accessioned | 2019-01-31T05:44:43Z | - |
| dc.date.available | 2019-01-31T05:44:43Z | - |
| dc.date.issued | 2019-01-31T05:44:43Z | - |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/122238 | - |
| dc.description.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. | en_US |
| dc.format.extent | 691553 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation | Artificial Neural Nets and Genetic Algorithms pp 397-400 | en_US |
| dc.title | Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data | en_US |
| dc.type | book/chapter | - |
| dc.identifier.doi | 10.1007/978-3-7091-6492-1_87 | - |
| dc.doi.uri | https://doi.org/10.1007/978-3-7091-6492-1_87 | - |
| item.fulltext | With Fulltext | - |
| item.cerifentitytype | Publications | - |
| item.openairetype | book/chapter | - |
| item.grantfulltext | open | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
| Appears in Collections: | 專書/專書篇章 | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6492-1_87.pdf | 675.34 kB | Adobe PDF2 | View/Open |
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