姓名 劉志峰 (Chih-Feng Liu) 電子郵件信箱 E-mail 資料不公開 畢業系所 電機工程學系研究所(Electrical Engineering) 畢業學位 博士(Ph.D.) 畢業時期 101學年第1學期 論文名稱(中) 神經模糊建模技術在預測中的應用 論文名稱(英) Application of Neuro-Fuzzy Modeling in Prediction 檔案
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論文語文/頁數 中文/122 統計 本論文已被瀏覽 5107 次，被下載 1 次 摘要(中) 本研究以神經模糊建模的技術來進行股票預測，當蒐集到的預測資料龐大時，有效將資料簡化便成為一項重要工作。所以本研究提出以相似度為基礎的資料縮減演算法，以減少監督式學習的訓練集規模。訓練樣本逐一輸入演算法後，透過相似度測試分組成一群，每一群的統計平均值皆視為代表該群中所有樣本的原型。接著，這些平均值的集合可用來取代原有訓練集，從而縮減日後進行監督式學習時所使用的訓練集。此一方法具有每群中所含資料的分佈獲得統計上的詳細說明，所獲得的每個原型資料都是相應群中所含樣本的良好代表，它會根據原始訓練樣本之間的相似度關係和分佈，自動萃取不同的代表數等優點。此外本研究提出的方法可以有效地同時應用於迴歸問題和分類問題，實驗結果顯示，本研究提出的方法比其他資料縮減方法更有效。
摘要(英) We propose a similarity-based prototype reduction algorithm to reduce the training set size for supervised learning. Training patterns are input to the algorithm one by one and grouped into blobs through similarity tests. The statistical mean of each blob is regarded as a prototype representing all the patterns included in the blob. The collection of such means can then be used to substitute the original training set, and, consequently, the training set for later supervised learning is reduced. This approach has several advantages. The distribution of the data contained in each blob is statistically well described. Each obtained prototype is a good representative of the patterns included in the corresponding blob. Different numbers of representatives are extracted auto- matically according to the similarity relationship among and the distribution of the original training patterns. Furthermore, our method can be applied efficiently to both regression and classification problems. Experimental results show that the proposed method performs more effectively than other prototype reduction methods.
Moreover, we present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base. Then the antecedent and consequent parameters associated with the rules are refined by particle swarm optimization and least squares estimation. Experimental results, obtained by running on several datasets taken from TAIEX and NASDAQ, demonstrate the effectiveness of the type-2 neuro-fuzzy modeling approach in stock price prediction.
關鍵字(中) 資料縮減方法 第Ⅱ型模糊集 TSK規則 自建構式模糊分群 最小平方估計法 粒子群優演算法 關鍵字(英) prototype reduction approach type-2 fuzzy set TSK rule self-constructing fuzzy clustering least squares estimation PSO 論文目次 中文摘要 Ⅲ
第 一 章緒論 1
第 二 章股票預測介紹 6
第 三 章支持向量機與軟式計算 11
第 四 章系統建模技術與應用 37
第 五 章實驗與結果 70
5.1.3實驗三: SBPR不同設定值的影響 76
5.2.2實驗二: TAIEX和TAIFEX資料預測 86
5.2.3實驗三: TAIEX、DJTA和NASDAQ資料預測 89
5.2.4實驗四: T2NFS與SVM方法比較 94
第 六 章結論與未來研究方向 97
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口試委員 周裕達 - 召集委員
侯俊良 - 委員
吳志宏 - 委員
歐陽振森 - 委員
蔡賢亮 - 委員
蔡賢亮 - 委員
鍾澍強 - 委員
李錫智 - 指導教授
口試日期 2012-12-19 繳交日期 2013-01-21