The Design and Application of a Self-organized Fuzzy System
碩士 === 華梵大學 === 機電工程研究所 === 89 === The adjustment of the premise and consequent part of fuzzy if-then rules is the most important issue in fuzzy learning problem. This thesis presents a method combing self-organization and least square estimation to automatically adjust the parameters of a fuzzy sys...
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ndltd-TW-089HCHT06570072015-10-13T12:43:59Z http://ndltd.ncl.edu.tw/handle/68104486411680192588 The Design and Application of a Self-organized Fuzzy System 自組式模糊系統的設計與應用 Chun-Te Hsu 徐俊德 碩士 華梵大學 機電工程研究所 89 The adjustment of the premise and consequent part of fuzzy if-then rules is the most important issue in fuzzy learning problem. This thesis presents a method combing self-organization and least square estimation to automatically adjust the parameters of a fuzzy system from training pattern. In chapter 2, we perform this method by using a feed-forward Tagagi-Sugeno-type fuzzy network on a typical plant of an inverted pendulum, and demonstrate the better convergent rate and average learning error when compared with some other traditional networks. In chapter 3, the proposed method is applied to a recurrent Tagagi-Sugeno-type fuzzy network, and the comparisons between the performance of using feed-forward and recurrent Tagagi-Sugeno-type fuzzy network are widely studied by an example of identification for a nonlinear system. Finally, the merits and the drawbacks of the proposed hybrid method will be discussed for different kinds of learning objects, and we also cite the thought on the direction of application for the future. Chiang-Ju Chien 簡江儒 2001 學位論文 ; thesis 77 en_US |
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碩士 === 華梵大學 === 機電工程研究所 === 89 === The adjustment of the premise and consequent part of fuzzy if-then rules is the most important issue in fuzzy learning problem. This thesis presents a method combing self-organization and least square estimation to automatically adjust the parameters of a fuzzy system from training pattern. In chapter 2, we perform this method by using a feed-forward Tagagi-Sugeno-type fuzzy network on a typical plant of an inverted pendulum, and demonstrate the better convergent rate and average learning error when compared with some other traditional networks. In chapter 3, the proposed method is applied to a recurrent Tagagi-Sugeno-type fuzzy network, and the comparisons between the performance of using feed-forward and recurrent Tagagi-Sugeno-type fuzzy network are widely studied by an example of identification for a nonlinear system.
Finally, the merits and the drawbacks of the proposed hybrid method will be discussed for different kinds of learning objects, and we also cite the thought on the direction of application for the future.
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Chiang-Ju Chien |
author_facet |
Chiang-Ju Chien Chun-Te Hsu 徐俊德 |
author |
Chun-Te Hsu 徐俊德 |
spellingShingle |
Chun-Te Hsu 徐俊德 The Design and Application of a Self-organized Fuzzy System |
author_sort |
Chun-Te Hsu |
title |
The Design and Application of a Self-organized Fuzzy System |
title_short |
The Design and Application of a Self-organized Fuzzy System |
title_full |
The Design and Application of a Self-organized Fuzzy System |
title_fullStr |
The Design and Application of a Self-organized Fuzzy System |
title_full_unstemmed |
The Design and Application of a Self-organized Fuzzy System |
title_sort |
design and application of a self-organized fuzzy system |
publishDate |
2001 |
url |
http://ndltd.ncl.edu.tw/handle/68104486411680192588 |
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