政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124331
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 110387/141319 (78%)
造访人次 : 46949775      在线人数 : 408
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/124331


    题名: The Sequentially-Learning-Based Algorithm with Multiple Output Nodes in Futures Forecast
    單層學習神經網路配合多輸出節點應用於期貨預測
    作者: Jheng, Yu-Jie
    鄭玉婕
    Tsai, Yu-Han
    蔡羽涵
    Tsaih, Rua-Huan
    蔡瑞煌
    贡献者: 2019智慧企業資訊應用發展國際研討會
    关键词: ASLFN, Cramming, Softening, Future forecast
    日期: 2019-06
    上传时间: 2019-07-17 15:05:00 (UTC+8)
    摘要: Investment is a way to increase assets. The types of investment are very diverse, including stocks, futures, funds and so on. Regardless of the form of investment, the consistent purpose of investors is to make a profit. Inevitably, these investment commodities are accompanied by risks, but different investment products have different risks and profitability. In the past, technical analysis usually used statistical methods to analyze the market. Although the results have reference value, the effect is limited. The reason is that whether it is stocks or futures, the trend of the two is highly nonlinear. AI has different characteristics that can break through the limitations of traditional analysis because it involves multidimensional explanatory variables and uses a large number of continuous long-term records to achieve more accurate prediction requirements and reasonable business insight. This study addresses this challenge through deriving a sequentially-learning-based algorithm for the single-hidden layer feed-forward neural networks (SLFN) with the binary input/output and making the technical justification. Within the training process, the amount of adopted hidden nodes is variable, and thus the SLFN becomes an adaptive single-hidden layer feed-forward neural networks (ASLFN).
    投資類型非常多樣化不同的投資產品具有不同的風險和盈利能力。過去,技術分析通常使用統計方法來分析市場。雖然結果具有參考價值,但效果有限。原因在於無論是股票還是期貨,兩者的走勢都是高度非線性。AI具有不同的特徵,可以突破傳統分析的局限性,因為它涉及多維解釋變量,並使用大量連續的長期記錄來實現更準確的預測要求和合理的業務洞察力。本研究通過利用二進制輸入/輸出為單隱藏層前饋神經網絡,在訓練過程中,模擬人腦的學習方式,採用的隱藏節點數量是可變的,因此SLFN成為自適應單層前饋式神經網絡。
    關聯: 2019智慧企業資訊應用發展國際研討會
    数据类型: conference
    显示于类别:[2019智慧企業資訊應用發展國際研討會] 會議論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    11.pdf74KbAdobe PDF2295检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈