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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)130-136
Number of pages7
JournalInternational Journal of Fuzzy Systems
Issue number2
Publication statusPublished - 2009 Jun


  • Fuzzy neural networks
  • Genetic algorithms
  • Hierarchical structures

ASJC Scopus subject areas

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


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