A dynamic hierarchical fuzzy neural network for a general continuous function

A serious problem limiting the applicability of the fuzzy neural networks is the "curse of dimensional-ity", especially for general continuous functions. A way to deal with this problem is to construct a dy-namic hierarchical fuzzy neural network. In this pa-per, we propose a two-stage genetic algorithm to in-telligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a ge-netic algorithm which is popular for flowshop sched-uling problems (GA-FSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) op-timizes the HFNN constructed by GA-FSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.