篇名 | A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function |
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卷期 | 11:2 |
作者 | Wei-Yen Wang 、 I-Hsum Li 、 Shu-Chang Li 、 Men-Shen Tsai 、 Shun-Feng Su |
頁次 | 130-136 |
關鍵字 | hierarchical structures 、 genetic algorithms 、 Fuzzy neural networks 、 EI 、 SCI 、 SCIE 、 Scopus |
出刊日期 | 200906 |
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.