DocumentCode :
872096
Title :
Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality
Author :
Hong, Xia ; Harris, Chris J. ; Chen, Sheng
Author_Institution :
Dept. of Cybern., Univ. of Reading, UK
Volume :
34
Issue :
1
fYear :
2004
Firstpage :
598
Lastpage :
608
Abstract :
A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.
Keywords :
fuzzy neural nets; inference mechanisms; knowledge acquisition; knowledge based systems; least mean squares methods; optimisation; D-optimality; Gram-Schmidt method; Takagi-Sugeno inference mechanism; dynamical system; finite data set; information extraction; knowledge extraction; model matrix feature subspace; neurofuzzy model construction algorithm; neurofuzzy rule; optimal experimental design; orthogonal least squares algorithm; subspace decomposition; Data mining; Design for experiments; Energy states; Fuzzy sets; Fuzzy systems; Inference algorithms; Inference mechanisms; Matrix decomposition; Robustness; Takagi-Sugeno model;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2003.817089
Filename :
1262528
Link To Document :
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