臺灣博碩士論文加值系統

本研究提出一種基於遞迴式奇異值分解之線上模糊極限學習機，用以改良原始模糊極限學習機，使之適用於解決分類或迴歸建模中之線上學習問題。與原始模糊極限學習機相同，本方法中隱藏層模糊歸屬函數之相關權重乃是透過隨機給值的方式來設定。然而，本方法使用遞迴式奇異值分解取代原先摩爾彭洛斯廣義逆矩陣，用以針對逐筆輸入資料求出當時之最佳輸出層權重，因此適用於線上學習。實驗結果顯示，相較於原始模糊極限學習機，本方法可進行線上學習，並可達到一致的建模準確率。此外，本方法較他人之線上循序學習演算法更具有較佳之建模準確率與穩定性。

In this study, we propose an online fuzzy extreme learning machine based on the recursive singular value decomposition for improving the fuzzy extreme learning machine, and therefore making it applicable for solving online learning problems in classification or regression modeling. Like the original fuzzy extreme learning machine, our approach randomly assigns values to weights of fuzzy membership functions in the hidden layer. However, the Moore-Penrose pseudoinverse is replaced with the recursive singular value decomposition for calculating the optimal weights corresponding to the output layer. Compared with the original fuzzy extreme learning machine, our approach is applicable for the online learning of classification or regression modeling and produces the same modeling accuracy. Moreover, our approach possesses the better modeling accuracy and stability than the other approach, namely, online sequential learning algorithm.

第一章 緒論 1
一、研究背景與動機 1
二、研究目的 4
第二章 文獻探討 5
一、極限學習機 5
二、隱藏節點數的調整 6
三、隱藏層節點的改良 7
四、線上學習演算法 8
五、改善解釋性與推廣性 9
第三章 研究方法 13
第四章 實驗結果 17
一、資料集介紹 17
(一) 迴歸資料集介紹 17
(二) 分類資料集介紹 21
(三) 實驗流程 24
二、實驗結果 25
(一) 迴歸資料集實驗結果 26
(二) 分類資料集實驗結果 31
(三) 實驗總結 36
結論與未來展望 37
參考文獻 38

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