A dynamic hierarchical fuzzy neural network for a general continuous function

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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 (GA_FSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GA_FSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
Pages1318-1324
Number of pages7
DOIs
Publication statusPublished - 2008 Nov 7
Event2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 - Hong Kong, China
Duration: 2008 Jun 12008 Jun 6

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Other

Other2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
CountryChina
CityHong Kong
Period08/6/108/6/6

Keywords

  • Fuzzy neural networks
  • Genetic algorithms
  • Hierarchical structures

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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  • Cite this

    Wang, W. Y., Li, I. H., Li, S. C., Tsai, M. S., & Su, S. F. (2008). A dynamic hierarchical fuzzy neural network for a general continuous function. In 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 (pp. 1318-1324). [4630543] (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZY.2008.4630543