A dynamic hierarchical fuzzy neural network for a general continuous function

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

doi:10.1109/fuzzy.2008.4630543

A dynamic hierarchical fuzzy neural network for a general continuous function

Wei-Yen Wang Wei-Yen Wang, Shun-Feng Su Shun-Feng Su + Show 3 more

https://doi.org/10.1109/fuzzy.2008.4630543

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Publication Date: Jun 1, 2008

Citations: 16

Affiliation: National Taiwan Normal University, National Taiwan University of Science and Technology, National Taipei University of Technology, Shandong Institute of Automation

#Hierarchical Neural Network #Dynamic Neural Network + Show 8 more

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

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