Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition
碩士 === 義守大學 === 資訊工程學系 === 105 === 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 reg...
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ndltd-TW-105ISU053920332019-05-15T23:39:36Z http://ndltd.ncl.edu.tw/handle/957pjj Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition 基於遞迴式奇異值分解之線上模糊極限學習機 Yu-Yuan Cheng 鄭育淵 碩士 義守大學 資訊工程學系 105 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. Chen-Sen Ouyang 歐陽振森 2017 學位論文 ; thesis 48 zh-TW |
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碩士 === 義守大學 === 資訊工程學系 === 105 === 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.
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Chen-Sen Ouyang |
author_facet |
Chen-Sen Ouyang Yu-Yuan Cheng 鄭育淵 |
author |
Yu-Yuan Cheng 鄭育淵 |
spellingShingle |
Yu-Yuan Cheng 鄭育淵 Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition |
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Yu-Yuan Cheng |
title |
Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition |
title_short |
Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition |
title_full |
Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition |
title_fullStr |
Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition |
title_full_unstemmed |
Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition |
title_sort |
online fuzzy extreme learning machine based on recursive singular value decomposition |
publishDate |
2017 |
url |
http://ndltd.ncl.edu.tw/handle/957pjj |
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