| Title: | Fuzzy regression with radial basis function network |
| Authors: | 鄭啟斌;Lee, E. S. |
| Contributors: | 淡江大學資訊管理學系 |
| Keywords: | Regression analysis;Nonparametric fuzzy regression;Fuzzy radial basis network |
| Date: | 2001-09-28 |
| Issue Date: | 2011-10-23 13:17:07 (UTC+8) |
| Abstract: | 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. |
| Relation: | Fuzzy Sets and Systems 119, pp.291-301 |
| DOI: | 10.1016/S0165-0114(99)00098-6 |
| Appears in Collections: | [資訊管理學系暨研究所] 期刊論文
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