A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function

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Title:	A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function
Authors:	W. Y. Wang, I.H. Li, S.C. Li, M.S. Tsai, and S.F Su
Keywords:	Fuzzy systems;Conferences
Date:	2008-09-23
Issue Date:	2019-03-13 12:10:22 (UTC+8)
Publisher:	Springer
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 (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.
Relation:	International Journal of Fuzzy Systems 11(2), p.130-136
DOI:	10.1109/FUZZY.2008.4630543
Appears in Collections:	[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Journal Article

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