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    Please use this identifier to cite or link to this item: http://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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