A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function
學年 97
學期 1
出版（發表）日期 2008-09-23
作品名稱 A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function
作品名稱（其他語言）
著者 W. Y. Wang, I.H. Li, S.C. Li, M.S. Tsai, and S.F Su
單位
出版者
著錄名稱、卷期、頁數 International Journal of Fuzzy Systems 11(2), p.130-136
摘要 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.
關鍵字 Fuzzy systems;Conferences
語言 en
ISSN 1098-7584
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 DEU
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115952 )