Translated Titles

Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition



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



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Chinese Abstract


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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