國立臺灣師範大學電機工程學系W.-Y. WangI-H. LiS.-C. LiM.-S. TsaiS.-F. Su2014-10-302014-10-302008-06-06http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/32015A 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.hierarchical structuresgenetic algorithmsFuzzy neural networks