修改支援向量機模型於預測系統之應用 林志昇、白炳豐
E-mail: [email protected]
摘 要
支援向量機(SVMs)模式為一種新的類神經網路，目前以成功的解決非線性 迴歸估計問題。在真實的時間序列中，是一個複 雜和非線性動態的系統， 在複雜的時間序列中，有效的預測是一種非當重要的題目，因此，預測系 統是非常複雜的，而 且，有不同的方法去預測，一般，單一的預測模式是 非常固難的去預測初雜的時間序列，包含了支援向量機(SVMs)，因些
，本 研究修改支援向量機(SVMs)模式去處理時間序列的預測。
關鍵詞 : 支援向量機，時間序列，類神經網路。
目錄
封面內頁 簽名頁 授權書………...iii 中文摘要………
……….…v ABSTRACT……….……...……….………….vi 誌謝…...……….……...…
………vii 目錄…...……….……...……….viii 圖目錄……….……...…
……….x 表目錄………....………xi 第一章 緒論………...…………
……….1 1.1 研究背景與動機………..……...……….……...1 1.2 研究目的………..………
…..………...…………...……...2 1.3 研究方法..………..……….…...……....…...3 1.4 研究流程..………..………
……….…...……....…...5 第二章 文獻探討 .……….……….………8 2.1 傳統預測方法……….…
……..………...8 2.2 類神經網路…………..……..…………...…..………...10 2.3 支援向量機…………..……..……
……...…..………...14 2.4 混合式模型…………..……..…………...…..…………...17 2.5 遞迴式類神經網路…..……..………
…...…..…………...17 第三章 研究方法……….………..19 3.1 支援向量機模型.……..………...
…………..….……...19 3.2 混合式支援向量機模型….……...………..…...22 3.3 遞迴式支援向量機模型………
…………....……...23 第四章 預測實例……….………...………….………27 4.1 例子1..………..…………
………...27 4.2 例子 2………..………...40 第五章 結論及末來研究方向…...…….
……….………48 5.1 結論…..………..………...48 5.2 末來研究方向…………..………
………...49 5.2.1 遞迴式支援向量機模型………..…...49 5.2.2 資料採礦模型…………...……
………...51 參考文獻………...…….………54 參考文獻
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