教師資料查詢 | 類別: 期刊論文 | 教師: 翁慶昌 Wong Ching-chang (瀏覽個人網頁)

標題: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
語言英文(美國)
ISSN2168-2216;2168-2232
期刊性質國外
收錄於
產學合作
通訊作者
審稿制度
國別美國
公開徵稿
出版型式,電子版,紙本
相關連結
Google+ 推薦功能,讓全世界都能看到您的推薦!