Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/32015
Title: A dynamic hierarchical fuzzy neural network for a general continuous function
Authors: 國立臺灣師範大學電機工程學系
W.-Y. Wang
I-H. Li
S.-C. Li
M.-S. Tsai
S.-F. Su
Issue Date: 6-Jun-2008
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.
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/32015
Other Identifiers: ntnulib_tp_E0604_02_038
Appears in Collections:教師著作

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