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研究生:林志昇
研究生(外文):Chih-Sheng Lin
論文名稱:修改支援向量機模型於預測系統之應用
論文名稱(外文):Modified SVMs Model in Forecasting System
指導教授:白炳豐白炳豐引用關係
指導教授(外文):Ping-Feng Pai
學位類別:碩士
校院名稱:大葉大學
系所名稱:工業工程學系碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
中文關鍵詞:支援向量機時間序列類神經網路
外文關鍵詞:support vector machinestime seriesneural networks
相關次數:
  • 被引用被引用:4
  • 點閱點閱:264
  • 評分評分:
  • 下載下載:45
  • 收藏至我的研究室書目清單書目收藏:4
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。
Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. In the real-world time series is a complex and nonlinear dynamic system. Effective time series forecasting is one of the most important topics in the complex time series. Therefore, forecasting system is very complicated and thus difficult to predict. In general, it is very hard for an individual model including SVMs model to complex time series. It is not satisfactory by Cao. Therefore, I modify the SVMs model to deal with the time series forecasting.
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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