A dynamic hierarchical fuzzy neural network for a general continuous function

Wei-Yen Wang, I. Hsum Li, Shu Chang Li, Men Shen Tsai, Shun Feng Su

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9 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)130-136
頁數7
期刊International Journal of Fuzzy Systems
11
發行號2
出版狀態已發佈 - 2009 六月 1

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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