Title

基於遞迴式奇異值分解之線上模糊極限學習機

Translated Titles

Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition

Authors

鄭育淵

Key Words

極限學習機 ; 類神經網路 ; 模糊系統 ; 模糊推論系統 ; 遞迴式奇異值分解 ; 線上學習 ; 模糊極限學習機 ; extreme learning machine ; artificial neural network ; fuzzy system ; fuzzy inference system ; recursive singular value decomposition ; online learning ; fuzzy extreme learning machine

PublicationName

義守大學資訊工程學系學位論文

Volume or Term/Year and Month of Publication

2017年

Academic Degree Category

碩士

Advisor

歐陽振森

Content Language

繁體中文

Chinese Abstract

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。

English Abstract

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.

Topic Category 基礎與應用科學 > 資訊科學
電機資訊學院 > 資訊工程學系
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