政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/125529

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题名:	單層學習神經網路配合多輸出節點應用於期貨預測 The Single-hidden Layer Feedforward Neural Networks with Multiple Output Nodes for Futures Forecast
作者:	鄭玉婕 Jheng, Yu-Jie
贡献者:	蔡瑞煌 Tsaih, Rua-Huan 鄭玉婕 Jheng, Yu-Jie
关键词:	人工神經網絡強記、軟化與整合混合人工智能期貨預測決策支持系統 Artificial Neural Network Cramming and Softening and Integrating Hybrid Artificial Intelligence Futures Forecast Decision Support System
日期:	2019
上传时间:	2019-09-05 15:44:43 (UTC+8)
摘要:	蔡，許和賴（1998）提出了一種混合人工智能（AI）方法，該方法集成了基於規則的系統和人工神經網絡（ANN）技術，用以預測標準普爾500指數期貨未來價格變化的方向。他們聲稱混合方法可以促進更可靠的智能係統的開發，以模擬專家思維和支持決策過程。　　這項研究在兩個方面與蔡，許和賴（1998）提出的混合人工智能（AI）有所不同。首先，本研究有兩個新增的狀態變量用於描述市場狀態。其次，我們使用單層前饋式神經網絡（SLFN）和強記、軟化和整合（CSI）學習算法代替推理神經網絡（RN）和反向傳播學習算法。　　實驗結果表明，所提出的具有CSI學習算法的決策支持系統可有效預測2007年至2013年7年測試期間的Non-obvious和Unobserved的資料。決策支持系統為使用者在做決策時提供建議。　　Tsaih, Hsu and Lai (1998) proposed a hybrid artificial intelligence (AI) method that integrates rule-based system techniques and artificial neural network (ANN) techniques to predict the direction of future S&P 500 index futures price changes. They claim that hybrid approaches can facilitate the development of more reliable intelligent systems to simulate expert thinking and support decision-making processes. 　　This study differs from Tsaih, Hsu & Lai (1998) in two ways. First, the study has two additional state variables for the research purpose. Secondly, we use the single hidden layer feedforward neural network (SLFN) and the Cramming, Softening and Integrating (CSI) learning algorithm instead of the Reasoning Neural Networks (RN) and the Back Propagation learning algorithm. 　　The empirical results show that the proposed decision support system with CSI learning algorithm is effective in predicting Non-obvious and Unobserved data during the 7-year test period from 2007 to 2013. The decision support system provides advice to the user when making decisions.
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描述:	碩士國立政治大學資訊管理學系 106356019
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0106356019
数据类型:	thesis
DOI:	10.6814/NCCU201900839
显示于类别:	[資訊管理學系] 學位論文

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