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研究生:周小萍
研究生(外文):Chou, hsiaoping
論文名稱:細菌進化粒子群最佳演算法設計財務時間序列模糊預測系統
論文名稱(外文):Bacterial Foraging Particle Swarm Optimization Algorithm Time-Serial Fuzzy Prediction System Design
指導教授:馮玄明呂謙呂謙引用關係
指導教授(外文):Feng, HsuanMingLyu, Chian
口試委員:高文星潘進儒
口試委員(外文):Kao, WuenHsiengPan, JienRu
口試日期:2012-06-21
學位類別:碩士
校院名稱:國立金門大學
系所名稱: 觀光管理學系
學門:民生學門
學類:觀光休閒學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:75
中文關鍵詞:預測
外文關鍵詞:pso
相關次數:
  • 被引用被引用:0
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  • 下載下載:3
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本研究基於粒子群最佳演算法(Particle Swarm Optimization, PSO) 細菌進化粒子群最佳演算法(Bacterial Foraging Particle Swarm Optimization, BFPSO) 建構一個財務時間序列預測系統。本文方法係基於自動學習演算法所建立,主要包括以動態群集演算法(dynamic clustering-based learning algorithm)對輸入財務資料集進行分群,決定放射狀基底函數網路(Radial Basis Function Networks, RBFN)的基底函數數量並建構初始的系統原型,再利用PSO結合遞迴式最小平方法(recursive least square, RLS)的學習演算法來調整系統的參數。本論文也使用細菌進化粒子群最佳演算法調整模糊系統參數,可以快速且精確的描述財務資料所表現的走勢行為模式,並準確的對財務時間序列走勢進行預測。本文針對台灣加權股價指數在2000年至2004年期間的時間序列資料進行訓練與驗證,由實驗結果可知本研究所提出之方法可以有效的對股價的指數以及走勢進行預測,大幅提高交易勝算。
This study presents Particle Swarm Optimization (PSO) and Bacterial Foraging Particle Swarm Optimization (BFPSO) to develop the financial time-series prediction system. The dynamic clustering-based learning algorithm is first to determine the number of Radial Basis Function Networks (RBFN) and select the center positions of Radial Basis Function. Thus, the initial system model is fast determined. The particle swarm optimization (PSO) and recursive least square (RLS) learning machines are proposed to acquire the appropriate parameters of model system to predict the behavior of the identified stock data set. In this article, the novel BFPSO learning algorithm is proposed to efficiently achieve the fuzzy system to create the accurate prediction model. The identified model can actually forecast the stock data. The real Taiwan Stock Price Index serial data between 2000 and 2004 illustrate the efficiency of the proposed learning scheme. Several experiments in the stock price prediction examples can present the powerful of our illustrated learning algorithms.
目錄
國立金門大學碩士學位論文考試審定書 i
摘要 ii
Abstract iii
序 言 iv
目錄 v
圖目錄 vii
表目錄 viii
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研究範圍 2
1.4研究架構 3
第二章 文獻探討 5
2.1效率市場假說 5
2.2股價分析方法 7
2.3模糊系統簡介 9
2.4國內外相關文獻探討 15
第三章 研究方法 21
3.1模糊推論系統 21
3.2 FUZZY C-MEANS演算法 28
3.3粒子群最佳化演算法 29
3.3.1定義 29
3.3.2粒子群最佳化演算法設計 31
3.3.3粒子速度與位置的更新法則 32
3.3.4參數設定 33
3.4遞迴式最小平方法 34
3.4放射狀基底函數網路 37
3.5細菌進化粒子群最佳化演算法(BFPSO) 39
3.5.1細菌趨向性(taxis) 40
3.5.2群聚散佈(dispersal) 41
3.5.3繁殖(reproduction)與滅亡(elimination) 41
第四章 自動群集放射狀基底函數網路建模系統設計應用於股價預測 43
4.1自動群集演算法設計放射狀基底函數網路基本架構 43
4.2放射狀基底函數網路建模系統設計 45
4.3股價預測模擬 47
第五章 細菌進化粒子群最佳演算法設計財務時間序列模糊預測系統 51
5.1背景說明 51
5.2動態群集演算法建立模糊系統架構 52
5.3結合細菌進化粒子群最佳化演算法與遞迴式最小平方法調整模糊系統參數 54
5.3.1細菌趨向性(taxis) 55
5.3.2群聚散佈(dispersal) 56
5.3.3繁殖(reproduction)與滅亡(elimination) 56
5.4股價預測模擬 59
5.5模擬實驗 61
第六章 結論與展望 63
參考文獻 64
已發表或已接受之論文 70


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