期刊論文
| 學年 | 90 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2001-09-28 |
| 作品名稱 | Fuzzy regression with radial basis function network |
| 作品名稱（其他語言） | |
| 著者 | 鄭啟斌; Lee, E. S. |
| 單位 | 淡江大學資訊管理學系 |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Fuzzy Sets and Systems 119, pp.291-301 |
| 摘要 | Radial basis function network is used in fuzzy regression analysis without predefined functional relationship between the input and the output. The proposed approach is a fuzzification of the connection weights between the hidden and the output layers. This fuzzy network is trained by a hybrid learning algorithm, where self-organized learning is used for training the parameters of the hidden units and supervised learning is used for updating the weights between the hidden and the output layers. The c-mean clustering method and the k-nearest-neighbor heuristics are used for the self-organized learning. The supervised learning is carried out by solving a linear possibilistic programming problem. Techniques for the generalization of the network are also proposed. Numerical examples are used to illustrate and to test the performances of the approach. |
| 關鍵字 | Regression analysis;Nonparametric fuzzy regression;Fuzzy radial basis network |
| 語言 | en |
| ISSN | 0165-0114 1872-6801 |
| 期刊性質 | 國外 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | NLD |
| 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/987654321/68571 ) |