A dynamic hierarchical fuzzy neural network for a general continuous function
| dc.contributor | 國立臺灣師範大學電機工程學系 | zh_tw |
| dc.contributor.author | W.-Y. Wang | en_US |
| dc.contributor.author | I-H. Li | en_US |
| dc.contributor.author | S.-C. Li | en_US |
| dc.contributor.author | M.-S. Tsai | en_US |
| dc.contributor.author | S.-F. Su | en_US |
| dc.date.accessioned | 2014-10-30T09:28:20Z | |
| dc.date.available | 2014-10-30T09:28:20Z | |
| dc.date.issued | 2008-06-06 | zh_TW |
| dc.description.abstract | A serious problem limiting the applicability of the fuzzy neural networks is the "curse of dimensionality", especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GAFSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GAFSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market. | en_US |
| dc.description.uri | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4630543 | zh_TW |
| dc.identifier | ntnulib_tp_E0604_02_038 | zh_TW |
| dc.identifier.uri | http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/32015 | |
| dc.language | en | zh_TW |
| dc.relation | IEEE International Conference on Fuzzy Systems,Hong Kong, pp.1318-1324 | en_US |
| dc.subject.other | hierarchical structures | en_US |
| dc.subject.other | genetic algorithms | en_US |
| dc.subject.other | Fuzzy neural networks | en_US |
| dc.title | A dynamic hierarchical fuzzy neural network for a general continuous function | en_US |