A method of constructing fuzzy neural network based on rough set theory

A method of constructing fuzzy neural network structure by using rough set theory is presented . Since rough set theory has strong ability of analyzing numerical value and fuzzy neural network has the ability of approximating function nicely, a neural network model which has good intelligibility, si...

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Published inProceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693) Vol. 3; pp. 1723 - 1728 Vol.3
Main Authors Xian-Ming Huang, Ji-Kai Yi, Yan-Hong Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Arithmetic
Biological neural networks
Computer networks
Control engineering
Fuzzy control
Fuzzy logic
Fuzzy neural networks
Fuzzy set theory
Neural networks
Set theory
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Summary:A method of constructing fuzzy neural network structure by using rough set theory is presented . Since rough set theory has strong ability of analyzing numerical value and fuzzy neural network has the ability of approximating function nicely, a neural network model which has good intelligibility, simple computation and fast convergence is constructed by combining both theory. The main process to construct this network is as follows: firstly to acquire rules from present data set by rough set theory; then the cell number of each layer and relevant initial parameters are constructed according to these rules; finally all kinds of parameters are computed by BP(back promulgation) arithmetic and the design of the network is finished. Also in this paper an example of approximating a 2D nonlinear function is discussed and the feasibility and validity of the method are proved.
ISBN:0780378652
9780780378650
9780780381315
0780381319
DOI:10.1109/ICMLC.2003.1259775