| A hybrid clustering and gradient descent approach for fuzzy modeling | |
|---|---|
| 學年 | 88 |
| 學期 | 1 |
| 出版（發表）日期 | 1999-12-01 |
| 作品名稱 | A hybrid clustering and gradient descent approach for fuzzy modeling |
| 作品名稱（其他語言） | |
| 著者 | 翁慶昌; Wong, Ching-chang; Chen, C.C. |
| 單位 | 淡江大學電機工程學系 |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IEEE transactions on systems, man and cybernetics 29(6), pp.686-693 |
| 摘要 | In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach. |
| 關鍵字 | Fuzzy systems;Parameter estimation;Clustering algorithms;Fuzzy sets;Mathematical model;Clustering methods;Nonlinear systems;Inference algorithms;Uncertain systems;System identification |
| 語言 | en_US |
| ISSN | 2168-2216 2168-2232 |
| 期刊性質 | 國外 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/60752 ) |
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